ARTÍCULO DE INVESTIGACIÓN

The APC-barrier and its effect on stratification
in open access publishing

Thomas Klebel1

and Tony Ross-Hellauer1,2

1Open and Reproducible Research Group, Know-Center GmbH, Graz, Austria
2Open and Reproducible Research Group, Graz University of Technology, Graz, Austria

Palabras clave: article processing charge, equity, institutional resources, Open Access, estratificación

ABSTRACTO

Current implementations of Open Access (OA) publishing frequently involve article processing
charges (APCs). Increasing evidence has emerged that APCs impede researchers with fewer
resources in publishing their research as OA. We analyzed 1.5 million scientific articles from
journals listed in the Directory of Open Access Journals to assess average APCs and their
determinants for a comprehensive set of journal publications across scientific disciplines,
world regions, and through time. Levels of APCs were strongly stratified by scientific fields
and the institutions’ countries, corroborating previous findings on publishing cultures and
the impact of mandates of research funders. After controlling for country and scientific field
with a multilevel mixture model, sin embargo, we found small to moderate effects of levels of
institutional resourcing on the level of APCs. The effects were largest in countries with low
PIB, suggesting decreasing marginal effects of institutional resources when general levels of
funding are high. Our findings provide further evidence on how APCs stratify OA publishing
and highlight the need for alternative publishing models.

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INTRODUCCIÓN

1.
Science is central in today’s knowledge societies (Stehr, 1994), and is seen as essential to inno-
vation and economic prosperity (Miao, Murray et al., 2022). Todavía, science is not conducted in a
vacuum. Who performs science has implications on what is studied. Por ejemplo, diseases
and conditions primarily affecting women are strongly understudied in the medical sciences
(p.ej., Beery & Zucker, 2011; Joven, Pescador, & Kirkman, 2019), due to male researchers per-
forming less research directed at women. Similar trends can be found for other factors, como
race and ethnicity (p.ej., Deardorff, Hoyt et al., 2019; Tornero, Steinberg et al., 2022). For sci-
ence to meet humanity’s needs, scientific research should incorporate knowledge from a
diverse range of academics (see also Naik, Sugimoto et al. (2022))1.

A growing body of literature examines how knowledge generation is structured globally,
and highlights how scholarship from outside of the Global North is often deemed less relevant
or credible (Albornoz, Okune, & chan, 2020; Collyer, 2018), or overlooked (Gómez, Herman,
& Parigi, 2022). The open access (OA) movement initially raised hopes of leveling the playing

1 This is reflected, Por ejemplo, en el 2021 UNESCO Recommendations on Open Science, calling for Open
Science to “embrace a diversity of knowledge, practicas, workflows, idiomas, research outputs and
research topics that support the needs and epistemic pluralism of the scientific community as a whole”
(UNESCO, 2021).

un acceso abierto

diario

Citación: Klebel, T., & Ross-Hellauer, t.
(2023). The APC-barrier and its effect
on stratification in open access
publicación. Quantitative Science
Estudios, 4(1), 22–43. https://doi.org/10
.1162/qss_a_00245

DOI:
https://doi.org/10.1162/qss_a_00245

Revisión por pares:
https://www.webofscience.com/api
/gateway/wos/peer-review/10.1162
/qss_a_00245

Supporting Information:
https://doi.org/10.1162/qss_a_00245

Recibió: 15 Octubre 2022
Aceptado: 15 Enero 2023

Autor correspondiente:
Thomas Klebel
tklebel@know-center.at

Editor de manejo:
Juego Waltman

Derechos de autor: © 2023 Thomas Klebel and
Tony Ross-Hellauer. Published under a
Creative Commons Attribution 4.0
Internacional (CC POR 4.0) licencia.

La prensa del MIT

The APC-barrier and its effect on stratification

field somewhat (chan, Cuplinskas et al., 2002), by removing the barriers to accessing knowl-
edge posed by journal subscriptions (Matheka, Nderitu et al., 2014). Although more and more
research is becoming freely available (Cervecero, Priem et al., 2018), recent research shows that
one specific model of OA, publisher-hosted (oro) OA, funded by author-facing charges (arti-
cle processing charges, or APCs), is erecting a new barrier, preventing authors in less resourced
settings (countries, institutions, campos) from contributing to the scientific record (Albornoz
et al., 2020; Cabrerizo, 2022; Matheka et al., 2014; Olejniczak & wilson, 2020; Segado-
Boj, Martín-Quevedo, & Prieto-Gutiérrez, 2018; Siler, Haustein et al., 2018; Herrero, Merz
et al., 2021).

This potential new barrier has been examined on the level of individual authors, institu-
ciones, countries, and fields. Focusing on the individual level and investigating data from the
United States, Olejniczak and Wilson (2020) found a higher likelihood for publishing OA
articles that involve an APC for authors of male gender, from prestigious institutions, con
previous federal research funding, or an association with a STEM field. They conclude that
“[pag]articipation in APC OA publishing appears to be skewed toward scholars with greater
access to resources and job security.” Along similar lines, Niles, Schimanski et al. (2020)
reported that in a survey of Canadian and U.S. academics, publication costs were of higher
relevance for women than men in making publishing decisions. The role of institutional sup-
port in covering APCs is evidently of urgency for researchers without an affiliation to a
research-oriented institution. High APCs and APCs in general might preclude this growing seg-
ment of researchers from contributing to the scientific record (Burchardt, 2014; ElSabry, 2017;
Gray, 2020, pag. 1673).

Investigating the association between institutional characteristics and publishing outcomes,
Siler et al. (2018) found a clear hierarchy in publishing access outcomes. Analyzing a set of
articles from health research, they found that authors from lower ranked and presumably less
wealthy institutions were more likely to publish in toll-access journals, as well as in OA jour-
nals with no APC. A key driver for the move towards OA publishing is institutional policies,
with Huang, Neylon et al. (2020) finding clear signals for mandates to increase levels of OA
publicación. Además, the authors found publisher-mediated OA to be more common in
Latin American and African universities, which they attribute to the publishing infrastructure
in Latin America (p.ej., SciELO) and funder mandates in Africa.

These differences on individual and institutional levels are complemented by inequalities of
access to scientific publishing at the level of countries and regions. Researchers from the
Global South have more difficulties in paying increasingly high APCs simply due to lower pur-
chasing power parity (Demeter & Istratii, 2020). Waivers for APCs do exist but are not always
effective in countering this issue (Burchardt, 2014; Lawson, 2015; cf. Momeni, Dietze et al.,
2022). Investigating the geographic diversity of authors across 37,000 articles from Elsevier’s
“mirror-journal” system, Smith et al. (2021) found a lower geographic diversity of authors for
OA articles, and in particular, articles requiring an APC, than for non-OA articles. Los autores
conclude that their results provide support for the hypothesis that APCs “are a barrier to OA
publication by scientists from the low-income countries of the Global South.”

In assessing differences in APCs across contexts, scientific fields are an important mediating
factor. Studying average APC amounts for Gold and hybrid OA publishing, Björk and Solomon
(Björk & Solomon, 2015; Solomon & Björk, 2012b) found higher average APCs journals in
STEM, with substantially lower APCs in the Social Sciences and Humanities. These trends
might be partially associated with much higher external project funding in STEM than SSH
disciplines (Eve, 2014).

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The APC-barrier and its effect on stratification

The discrepancy in access to publishing is linked to the broader system of knowledge pro-
duction and its global distribution. Research from the Global North is often focused on phe-
nomena and viewpoints that are relevant to those countries. Research on issues relevant to
other regions or communities is commonly not deemed relevant by the Global North and sub-
sequently not accepted for publication in prestigious international journals. In the Global
South on the other hand, there is a strong focus on publishing “internationally,” that is, in jour-
nals published in the Global North (Collyer, 2018; Czerniewicz, 2015). Publications in highly
prestigious journals are even sometimes rewarded directly in terms of cash payments (a pesar de
this practice was abolished in 2020 in China [Mallapaty, 2020]), and are uniformly incentiv-
ized indirectly through higher chances to receive promotions (Czerniewicz, 2015). This leads
to a situation in which, for researchers from the Global South to publish Open Access in highly
regarded journals, they not only have to align their research with that of the North’s agenda,
but also to pay even higher APCs, as perceived journal prestige (represented by common mea-
sures such as the Impact Factor or the DOAJ SEAL) and levels of APCs are linked (Demeter &
Istratii, 2020; Gray, 2020; Maddi & Sapinho, 2022; Siler & Frenken, 2019). In economic terms,
research money from low-income countries (LIC) partly subsidizes the most prestigious pub-
lishing outlets, with researchers from less industrialized countries publishing considerably
more frequently in megajournals such as PLOS ONE than in the publisher’s more prestigious
counterparts like PLOS Biology (Ellers, Crowther, & harvey, 2017).

Finalmente, these tendencies might lead research published in local journals to become less
visible. As high-income countries enforce policies to publish OA, research from LIC which
might not yet be OA becomes even less visible (Albornoz, Huang et al., 2018; Czerniewicz,
2015). As local journals also usually have lower rankings on common metrics such as the
journal impact factor, research published in these journals not only receives less exposure
but might be perceived as to be of lesser quality (Gray, 2020).

Given initial evidence that the OA model involving APCs seems to be erecting a new bar-
rier for prospective authors, this paper extends and corroborates previous research in analyz-
ing average APCs and their determinants for a comprehensive set of journal publications
across scientific disciplines and world regions, and through time. We pay special attention
to the potential effect of institutional resources on APCs and their variation across contexts.
Al hacerlo, we provide important evidence to the discussion of how APCs shape publishing
resultados, which we hope will contribute to a more equitable implementation of OA publish-
ing in the future. Our analysis suggests that levels of institutional resourcing and average APCs
paid by researchers are related, even when controlling for contextual factors such as academic
discipline or country. This APC-Barrier highlights the need for alternative publishing models
that are inclusive to researchers irrespective of their institution’s level of support for APCs. En
Sección 2, we discuss how the data set was constructed and explain the methodological steps
taken throughout. Sección 3 introduces the main findings of the paper, combining an analysis
based on descriptive statistics with a formal hierarchical model. Sección 4 discusses the
findings and highlights implications, while acknowledging some limitations inherent to the
análisis.

2. MÉTODOS

For this study, we assembled a large bibliographic data set consisting of 1,572,417 publica-
ciones. These publications represent all publications published between 2009 y 2019 entre
journals listed in the Directory of Open Access Journals (DOAJ), where first and/or last authors
were affiliated with any institution listed in the 2021 CWTS Leiden Ranking.

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The APC-barrier and its effect on stratification

OpenAlex (Principal, Cervecero, & Orr, 2022) served as the primary source for bibliographic
datos. After the decommissioning of Microsoft Academic Graph (MAG) (Microsoft, 2021; Sinha,
Shen et al., 2015), OpenAlex now incorporates all data previously present in MAG, which was
further enriched by adding data from Unpaywall on Open Access publication status and other
identifiers. In using OpenAlex, we found identifiers for publication venues (p.ej., journals) ser
more reliable than in MAG, significantly improving the potential for matching with further data
sources. Scheidsteger and Haunschild (2022) compared MAG with OpenAlex in terms of cov-
erage and metadata accuracy and found that OpenAlex is at least as suited for bibliometric
analyses as MAG was. We used the OpenAlex snapshot that was released on April 7, 2022,
and converted the .json files to .csv files via python code supplied by the nonprofit organiza-
tion that built OpenAlex2. All further descriptive analysis of the data was conducted with
Spark, via the R package sparklyr (Luraschi, Kuo et al., 2022).

In line with previous research (p.ej., zhang, Wei et al. (2022); but see Butler, Matthias et al.
(2022) on a more thorough approach using historical data), we obtained data on APCs via the
public data dump from DOAJ, dated June 3, 2022. As of mid-2022, the DOAJ hosted a
community-curated list of close to 18,000 Open Access and peer-reviewed journals. Los datos
from DOAJ contains information on whether the journal imposes an APC and its amount in
varying currencies. To match data from DOAJ to OpenAlex, we used the linking ISSN table,
which we obtained from the ISSN International Centre on June 13, 20223. Starting from the list
of journals in DOAJ, containing 17,717 journals, we were able to match 15,640 (88.3%)
venues from OpenAlex. To unify the data on APC charges across currencies, we followed three
steps: if an amount was specified in U.S. dollars, we kept this record; if multiple currencies
were recorded, we preferred the version in U.S. dollars; and if the amount was not provided
in U.S. dollars, we converted it, using the exchange rates at
following
Gray (2020). During data pre-processing, we identified a few journals with erroneous values
for APCs, which we subsequently corrected.

Junio 4, 2022,

2.1. Assigning Publications to Institutions and Fields

To assign publications to institutions, we relied on the information provided in OpenAlex.
OpenAlex records the authors of each publication, and parses affiliation information to assign
authors to institutions. In the case of single authorships, the publication received a weight of
“1” towards the institution of the single author. In the case of multiple authors, we used full
counting among authors and fractional counting for authors affiliated with multiple institu-
ciones. The following example may illustrate the approach: A given publication P has two
authors A and B. Author A has one affiliation (a1), author B has two affiliations (a2, a3). El
subsequent weights for publication P were as follows: wa1 = 1, wa2 = 0.5, wa3 = 0.5. Nosotros
restricted our analysis to first and last authors, following the rationale that decisions on venues
and publishing models would usually be taken by the senior and/or the authors that contrib-
uted the most (Siler et al., 2018). Recent studies have used first and corresponding authors to
attribute publications (Simard, Ghiasi et al., 2022; Zhang et al., 2022). As OpenAlex does not
contain information on corresponding authors, we used first and last authors, which contribute
more to publications than middle authors (Larivière, Desrochers et al., 2016).

To assign publications to fields, we relied on the “concepts” provided with OpenAlex. Sim-
ilar to MAG, publications are tagged with concepts. The concepts in OpenAlex are identical
on the upper two levels of the hierarchy, whereas OpenAlex has a substantially reduced

2 https://gist.github.com/richard-orr/152d828356a7c47ed7e3e22d2253708d.
3 https://www.issn.org/services/online-services/access-to-issn-l-table/.

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The APC-barrier and its effect on stratification

number of concepts on the lower levels (Scheidsteger & Haunschild, 2022). For our analysis,
we relied on the top-level concepts only, which consist of 19 unique fields. OpenAlex further
provides a “score” for how strongly a given publication is linked to a given concept4. Nosotros estafamos-
structed an approach to fractional counting similar to that of institutions, but adapting it to
account for the uncertainty around tagging works. For each publication, we calculated the
weight towards a single concept c as

wc ¼

PAG

scorec
norte
c¼1 scorec

with score1, score2, …, scoren denoting all top-level concepts assigned to the given publication.

2.2. Country and Institution-Specific Indicators

2.2.1. Country indicators

Most institutions present in OpenAlex are assigned to a country, via data from MAG or ROR
(Research Organization Registry)5. To enrich the data from OpenAlex on countries with further
información, we used data from the World Bank, via the R package WDI (Arel-Bundock, 2022).
Específicamente, we matched the institutions’ countries with the general metadata tables to obtain
an indicator for world regions (“East Asia & Pacific,” “Europe & Central Asia," etc.). Matching
was conducted using the two-digit ISO code provided in both data sets. For data on country
income (PIB), we used the indicator “NY.GDP.PCAP.KD,” which refers to the GDP per capita
en 2015 constant U.S. dollars.

2.2.2.

Institutional indicators

For data on levels of institutional resourcing, we used the CWTS 2021 Leiden Ranking (Van
Eck, 2021), comprising 1,225 universities across 69 countries. We used the indicator Ptop 10%,
which is defined as “[t]he number […] of a university’s publications that, compared with other
publications in the same field and in the same year, belong to the top 10% más frecuentemente
cited“6. Previous research (Frenken, Heimeriks, & Hoekman, 2017) has emphasized the role of
university age and size when it comes to the level of resources available for supporting
research activities (through research equipment, graduate programs, libraries, institutional
assistance in securing grant funding, etc.). Por esta razón, we chose the size-dependent indi-
cator Ptop 10% over size-independent alternatives.

A three-step procedure was undertaken to match records from the Leiden Ranking to Open-
Alex. In the first step, we normalized university names and matched based on exact similarity.
Normalization included converting to lowercase, unifying encodings, removing commas, y
replacing “&” with “and.” Duplicate names (p.ej., two “University of Heidelberg” in
OpenAlex—one in Germany, and one in Ohio) were a rare issue, and were resolved by only
retaining matched universities where the countries listed in the Leiden Ranking and OpenAlex
also matched. In the second step, we manually matched the remaining universities by search-
ing for a given university name coming from the Leiden Ranking in OpenAlex. Common
examples of names that could not be matched automatically were the use of different lan-
calibres (“Technische Universität Berlin” vs. “Technical University of Berlin”; or “Universidade
de Lisboa” vs. “University of Lisbon”), different uses of linking words (“the” or “of”), or the use
of different name variants, Por ejemplo, by using abbreviations (ETH Zürich, VU Amsterdam,

4 https://docs.openalex.org/about-the-data/work#concepts.
5 https://docs.openalex.org/about-the-data/institution.
6 https://www.leidenranking.com/information/indicators.

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The APC-barrier and its effect on stratification

Cifra 1. Directed acyclic graph (DAG) of potential causal effects on levels of APCs. The effect of
institutional resources (“inst-resources”) on the amount of APC charges (“APC-amount”) is mediated
via the journal. Two main causal paths are marked in green: the direct effect on journal choice, y
the indirect effect via paper quality. Our model uses varying slopes for “inst-resources” across coun-
tries and fields, and varying intercepts for countries and fields themselves.

KU Lovaina, TU Wien, etc.). We used Google searches and Wikipedia to find common and
outdated name variants. We further used the links to Wikipedia entries and the institutions
ellos mismos, which are provided in OpenAlex, as well as the map provided in the online ver-
sion of the Leiden Ranking7. We were able to match all but one university, Resultando en 1,224
(99.9%) matched institutions.

2.3. Hierarchical Modeling

There are many different factors that might lead to differences in publishing outcomes. Cifra 1
depicts some of the most salient factors with a directed acyclic graph (DAG). DAGs allow us to
represent the causal assumptions of the studied phenomenon visually (Rohrer, 2018). Institu-
tional resources are assumed to contribute to APC levels through two pathways: directly and
indirectly via publication quality, where better resources might lead to higher publication
quality, which in turn influences where the manuscript might eventually be published. To esti-
mate the total causal effect of institutional resources on APC outcomes, we would need to
control for institutions and countries. To estimate the direct causal effect of institutional
resources on APC outcomes, we would additionally need to control for publication quality
(Pearl, Glymour, & Jewell, 2016). Given that different scientific fields exhibit highly varying
publishing cultures (p.ej., the different significance of book, journal, and conference publica-
ciones; varying degrees of acceptance towards preprints, etc.), it is reasonable to assume that
relationships might additionally be mediated by scientific fields.

To reduce potential biases in our estimates of the effect of institutional resources on levels of
APCs, we constructed a Bayesian multilevel mixture model that controls for scientific field and
country. Initially, we planned to also control for institutions. Sin embargo, our early models

7 Por ejemplo, https://www.leidenranking.com/ Ranking/ University2022?universityId=1187&fieldId=1
&periodId=12&fractionalCounting=1&performanceDimension=0&rankingIndicator=pp_top10&minNPubs
=100.

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The APC-barrier and its effect on stratification

suffered from nonidentifiability, because the Leiden Ranking only includes single universities
for multiple countries. The mixture components of the model address two particularities of the
dependent variable (APC amounts): More than two-thirds of journals in DOAJ charge no APC
en absoluto, and this should be incorporated into the model for a comprehensive analysis; y el
distribution of APC amounts is multimodal, with a peak for some fields at below U.S. $500, and a main peak for most fields at around U.S. $2,000. We hypothesize that this bimodal distribution
stems from differing strategies of publishers and traditions within fields, because the extent to
which APCs exhibit the bimodal tendency varies by field (Figure S7, Supplementary material).

Our modeling approach started with a hurdle model, combining a logistic regression for the
question of whether a given article had an APC or not, and a lognormal model for the APC
amount (conceptually similar to the analysis of Olejniczak and Wilson [2020]). As evidenced
by posterior predictive checks on the overall distribution of APC amounts, as well as when
making predictions for particular countries, the model did not fit the data well, due to the
bimodal distribution of nonzero values (Figures S8 and S9, Supplementary material). Para esto
reason, the model presented in this paper combines two hurdle models in one. The weight
given to the two model components is estimated alongside the other parameters, and modeled
with respect to the scientific field. Employing multilevel modeling allows us to estimate slopes
and intercepts even for countries with only a few universities, by partially pooling information
from across the whole data set (Gelman & Colina, 2009; McElreath, 2020). Although these esti-
mates might be more variable, we prefer including all data in the model, as rules for excluding
countries based on the number of universities or publications are bound to be arbitrary. adi-
cionalmente, the exclusion of smaller countries would bias results towards effects present in larger
institutions. Further details on the model, including choice of priors and strategies to counter
computational difficulties in fitting the model are provided in the supplement.

Because modeling the full data set with Bayesian inference was not feasible, we randomly
muestreado 8% of articles from the full data set for 2016–2019, which led to a sample size of
76,447. Given its size and the random sampling procedure, the sample is representative of
the whole data set.

The model uses log-transformations for both the dependent variable (APC amount) y el
independent variable (Ptop 10%), which is stratified by field and country. Given the model’s
complexity in having two hurdle components and two lognormal components, standard mea-
sures of directly interpreting coefficients to yield “marginal effects” were not applicable.
Igualmente, we did not deem commonly employed average marginal effects across the whole
sample to be informative, given the large share of publications coming from just three coun-
intentos (Porcelana, United States, Brasil). En cambio, we constructed comparable effect sizes across the
range of Ptop 10% by making predictions at the 20%, 50%, y 80% quantile (prediction A),
retaining the predicted draws from the posterior distribution. We then made predictions from
the model for the value of Ptop 10% at the three cut-offs raised by 1% (prediction B) and cal-
culated the ratio as β ¼ prediction B
prediction A. This yields values that can readily be interpreted in the stan-
dard interpretation of “log-log models,” where a 1% change in the independent variable (aquí:
Ptop 10%) leads to a change of β% in the dependent variable (aquí: APC value). The approach
can be understood as representing marginal effects at representative values. The Bayesian
nature of the model allowed us to construct compatibility regions by visualizing the density
of the computed ratios.

To analyze effects for fields, we predicted the ratios for all fields and countries, y luego
averaged over the effect across countries. En este sentido, the effects displayed below are not
average marginal effects across the whole sample but average marginal effects at

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The APC-barrier and its effect on stratification

representative values, weighting each country equally. To analyze effects for countries, nosotros
proceeded similarly, averaging over fields for the predictions of each country. This approach
combines effects across all model components (es decir., the modeled process for zero and nonzero
APCs, as well as the two components predicting the actual size of nonzero APCs).

2.4. Description of the Data

The full data set consists of 1,572,417 publicaciones. The most prevalent field in our data is
“Medicine,” with the share of fractional weights reaching 30.6% (Mesa 1, Figure S1). Contrary
to the general distribution of fields in MAG and OpenAlex, the second most common field is
“Biology” (18.5%). The social sciences are less prevalent in the overall sample, con, for exam-
por ejemplo, publications assigned to “History” amounting to 0.2% of all publications. The high prev-
alence of “Medicine” and “Biology” and low prevalence of other disciplines can be attributed
to two main reasons: Primero, the general distribution of publications across fields in OpenAlex,
y segundo, the prevalence of certain fields for journals listed in the DOAJ.

Similarmente, the proportion of publications assigned to countries and world regions is driven
by two processes: primero, the general distribution of countries across publications, y segundo,

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Field
Medicamento

Biología

Chemistry

Computer science

Materials science

Psicología

Environmental science

Physics

Political science

Geography

Sociology

Matemáticas

Arte

Negocio

Geology

Philosophy

Ciencias económicas

Historia

Ingeniería

Mesa 1.

Fractional publications by field

Fractional papers
481,047

290,499

163,767

140,438

106,550

98,461

46,768

44,382

36,339

31,787

29,340

24,093

21,573

20,992

13,937

10,596

5,709

3,379

2,760

Proportion
30.6%

18.5%

10.4%

8.9%

6.8%

6.3%

3.0%

2.8%

2.3%

2.0%

1.9%

1.5%

1.4%

1.3%

0.9%

0.7%

0.4%

0.2%

0.2%

29

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The APC-barrier and its effect on stratification

the number of institutions per country that are part of the Leiden Ranking. The number of uni-
versities in the Leiden Ranking follows a general division of research productivity, with coun-
tries such as China, the United States, Japón, and Germany having many universities in the
ranking, and smaller countries or countries with smaller footprints in the international publish-
ing landscape (such as Algeria, Luxembourg, Kuwait, Uganda, and Estonia) only having single
universities in the 2019 edition of the ranking.

The distribution of ranked universities across countries also broadly aligns with the overall
number of outputs produced in certain countries and world regions. Figure S2, Supplementary
material, displays the frequencies and counts across continents. En general, the largest share of
publications in our data set comes from universities in East Asia & Pacific (31.1%), seguido por
Europa & Central Asia (30.1%), North America (21.2%), and Latin America & Caribbean
(12.1%). There are few publications from the Middle East & North Africa (3.0%), South Asia
(1.2%), and Sub-Saharan Africa (1.2%).

3. RESULTADOS

3.1. Descriptive Findings

Taking a high-level view of the data, we find a moderate relationship between levels of insti-
tutional resourcing and average levels of APCs for the period 2016–2019 (Figura 2A). Assigning
publications to institutions by first and last author does not change the relationship. Given the
highly skewed nature of Ptop 10%, we show a log-linear relationship. In conceptual terms, este
means that a one-unit increase at lower levels of Ptop 10% is considered to be more relevant than
at high levels. To investigate how the association develops over time, we analyze the mean APC
amounts of the journals for the quartiles of the Ptop 10% distribución (Figura 2B). In line with the
cross-sectional view of Figure 1, we find that levels of institutional resourcing are associated
with the average levels of APCs of the journals in which the institutions’ researchers publish.
En particular, the highest quartile (the top 25% universities according to Ptop 10%) exhibits a sub-
stantially higher mean APC than all other quartiles. The stratification between the quartiles does
not change substantially over the observed period, with a slight decrease in the distance
between quartiles, and thus a slight reduction in inequality in terms of the APC amount. Given
that we use fixed values for APCs across the whole time period, the upward trend most likely
represents a shift in publishing patterns and is not driven by an increase in APCs.

3.1.1. Comparing scientific fields

When breaking down the association between institutional resources and levels of APCs
across fields, we find that the general pattern holds (Figura 3A): authors from higher ranked
institutions publish on average in journals with higher APCs. The strength of the association
differs between fields, and there is substantial nonlinearity. It should be noted that there is
much less data in smaller fields such as “Philosophy.” The estimate of the observed trends
is therefore more variable than in fields such as “Biology” and “Medicine.”

The differences in changes along Ptop 10% must also be understood in terms of differences in
overall levels of APCs, which differ substantially between fields (Figura 2B). Particularly high
APCs can be found for journals publishing research in “Biology” (average APC: A NOSOTROS. $2,118), followed by “Chemistry” (A NOSOTROS. $1,824) and “Medicine” (A NOSOTROS. $1,813). At the other end of the spectrum are journals publishing research in the social sciences and humanities, with average APCs of U.S. $59 in “Art,” U.S. $71 in “Philosophy,” and U.S. $170 in “Sociology.” The partic-
ularly low average APCs for these fields are to a large extent driven by a high share of publi-
cations in journals with no APC.

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Cifra 2. Association between institutional resources (Ptop 10%) and average APC. The APC is derived from the APC listed for each journal in
the DOAJ. Journals with no APC are included with an APC of U.S. $0. (A) displays the association for the time period 2016–2019. The x-axis is on a log10-scale—the displayed relationship is thus nonlinear. The line represents a general additive model fit with cubic splines (default settings from geom_smooth() from the ggplot2 R package). (B) displays the association over time, with Ptop 10% broken down into quartiles. We use fixed values for APCs across the whole time period. f por invitado 0 7 septiembre 2 0 2 3 Analyzing the association across fields over time (Figures S4 and S5, Supplementary material) we observe heterogeneous trends. Although in some fields (“Biology” and “Chemis- try”) average APCs have been rising from 2009 a 2019, other fields exhibit stable levels of APCs (p.ej., “Mathematics,” “Geography,” “Geology”) or downwards trends (“Physics”). Estafa- sidering the stratification within fields (as evidenced by the spread between quartiles), no clear pattern is discernible. The data suggest a narrowing of the gap between lowest and highest ranked institutions for “Biology” and “Chemistry,” and a potential slight increase in stratifica- tion for research in “Computer science” and “Sociology.” Given that our analysis uses static values for APCs per journal, this most likely represents a shift in where researchers tend to publish (p.ej., in journals with or without APCs, or with high or low APCs). Estudios de ciencias cuantitativas 31 The APC-barrier and its effect on stratification l D o w n o a d e d f r o m h t t p : / / directo . mi t . / e d u q s s / a r t i c e – pdlf / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 pd . f por invitado 0 7 septiembre 2 0 2 3 Cifra 3. APC by field. (A) shows the association between APC and Ptop 10% per field for the papers published in 2016–2019. Journals with no APC are included with an APC of U.S. $0. To aid interpretation, only selected fields are displayed in color, with the remaining fields plotted in
grey. An interactive version of the plot is available in the notebooks published with the code. (B) displays the average APC in the respective
campos (2009–2019). The average APC is obtained by multiplying the fractional contribution of each paper towards all fields with the APC of the
journal in which it is published and dividing by the sum of fractional contributions within each field. Journals with no APC are included with
an APC of U.S. $0. 3.1.2. Comparing countries Contrasting average APCs between countries, we observe substantial variation (Table S1, Supplementary material). We find the highest average APCs for researchers at institutions in Israel, Suiza, and Singapore, with average APCs of about U.S. $2,200. A diferencia de, el

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The APC-barrier and its effect on stratification

Cifra 4. Mean APC per country vs. GDP per capita. APCs are averaged across all years (2009–2016), GDP is expressed in 2015 constant
A NOSOTROS. dollars. Journals with no APC are included with an APC of U.S. $0. lowest average APCs can be observed for researchers at institutions in Colombia and Brazil, with average APCs below U.S. $500. To further explore the variation, we compare the average
APC per country with the country’s GDP per capita (Cifra 4). We observe high variation in aver-
age APCs for authors from countries with low to medium GDP per capita (< U.S. $30,000), ranging from U.S. $429 in Brazil to U.S. $2,002 in China. In contrast, the average APC for authors from countries with a GDP per capita above U.S. $30,000 consistently ranges from U.S. $1,700 to U.S. $2,200. Within the cluster of lower income countries, two key observa- tions can be made. First, the average APC for authors from Latin America and the Caribbean is consistently low, with the highest average APC among these countries in Mexico (U.S. $701). The low levels of APC in these countries likely are a result of local publishing cultures and infrastructure (e.g., SciELO), and potentially the emergence of local journals with low APCs. Second, in contrast, the average APC for authors from Sub-Saharan Africa is relatively high, ranging from U.S. $1,167 to U.S. $1,895. Given that for many countries only a few universities are listed in the Leiden Ranking, we recreated Figure 3 among all institutions present in OpenAlex (Figure S3, Supplementary material). Average APCs across countries are slightly lower, but the association between GDP per capita and average APCs is unchanged. The relatively high rates of average APCs in countries from Sub-Saharan Africa likely reflect the strong influence of research funding towards these countries, and subsequent mandates to publish OA, which has previously been suggested by Iyandemye and Thomas (2019). An exploratory analysis of the prevalence of field-specific publications across continents indeed reveals that the share of publications in “Medicine” from Sub-Saharan African countries is very high (42.8%), which lends support to this hypothesis. The rate of publications in “Medicine” is even higher in South Asia, whereas the average APC in South Asia is substantially lower. Here, we would suspect that these publications are to a lesser extent driven by third-party funding. The high average APC for China likely reflects its rise to one of the leading nations in science (Gomez et al., 2022; Xie, Zhang, & Lai, 2014; Zhou & Leydesdorff, 2006). Quantitative Science Studies 33 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification Figure 5. Association between institutional resources and mean APC across world regions. Jour- nals with no APC are included with an APC of U.S. $0. A purely descriptive analysis of associations between institutional ranking and average APC across countries is not possible, given that some countries have only a few ranked institutions. Figure 5 therefore displays the association broken down by continent, and we estimate indi- vidual country effects with a hierarchical mixture model in the next section. The descriptive analysis reveals that there are large disparities in terms of overall levels of APCs. The relation- ship between institutional ranking and mean APC is strongest in Europe & Central Asia, although the steep slope for low regions of Ptop 10% (from 30 to 100) should be interpreted with caution due to few data points in this region. The trends for all other continents are much more variable, also due to a low amount of data (few universities per continent) and we there- fore do not interpret their slopes. 3.2. Modeling the Effect of Institutional Resources Across Fields and Countries To separate the effect of Ptop 10% on levels of APCs from country and field effects, we used a Bayesian multilevel mixture model. Figure 6 displays the effect of a 1% increase in Ptop 10% on the level of APCs. For all fields except Mathematics, Physics, and Art, higher institutional resources are associated with higher APCs. For most fields, the effect is nonlinear in that it is more pronounced at lower levels of institutional resources than at higher levels. This might suggest support for the hypothesis that institutional resourcing influences submission choices of authors by enlarging or restricting the space of potential venues due to economic reasons. The effect of institutional resources on APCs is strongest in fields from the social sciences (“Political science,” “Sociology,” “Business”). Estimates for the arts and humanities (“Art,” “History,” “Philosophy”) are not uniform, but exhibit wide credibility regions. The wide inter- vals are a consequence of these fields exhibiting high rates of zero-APC publishing, which result in a low number of cases to estimate the nonzero component of the model. Effects in the natural and life sciences (“Biology,” “Medicine,” “Materials science,” “Chem- istry”) are estimated to be low, with narrow credibility intervals. The negative effect of institu- tional resources on APC amounts in “Mathematics” is mainly driven by the hurdle component of the model (i.e., authors from higher ranked institutions publish more frequently in journals with no APC at all in these fields, compared to the average of all fields). In contrast, for Quantitative Science Studies 34 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 6. Effect of an increase in Ptop 10% on the level of APC across fields. The dot represents the median effect averaged over the predicted effects across countries, with the lines representing 50% and 90% highest density intervals. research published in “Environmental science” and “Sociology,” authors from higher ranked institutions tend to publish in journals with no APC less than their peers from lower ranked institutions. Comparing the effect of institutional resources on the level of APCs across countries, we find small to moderate effects, with substantial heterogeneity in the estimates and their vari- ability. Figures S12 and S13 (Supplementary material) show the estimates split by continent, Quantitative Science Studies 35 The APC-barrier and its effect on stratification l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 7. Post hoc analysis of the effect of Ptop 10% on APC compared with country GDP, estimated at the median of Ptop 10% and averaged across all fields. Error bars represent the 50% highest density interval. and by low, middle, and high levels of Ptop 10% (as above). Almost all continents show a spread between countries with moderate negative effects versus those with moderate to large positive effects (e.g., New Zealand–Malaysia, Greece–Slovakia, Tunisia–Iran, Uganda–Nigeria). Ana- lyzing the effects across countries, we hypothesized that the differences in effect sizes might be related to overall levels of wealth. We therefore conducted a post hoc analysis, plotting the country estimates along the countries’ GDP per capita (Figure 6). This analysis indeed suggests that the effect of institutional resources tends to be stronger in countries with low levels of GDP per capita. Comparing the effect of institutional resources on APCs across countries (Figure 7) with overall levels of APCs per country (Figure 3) suggests a threshold effect: Institutional resources have a small effect on levels of APCs in countries where the overall APC level is high. This might be explained by general levels of resourcing. Alternatively, it could also point to country-specific policies on OA publishing. An additional observation of note is the low estimates for the effect of levels of institutional resourcing on levels of APCs among Sub- Saharan African countries. 4. DISCUSSION Open Science holds the promise to make research processes more transparent, efficient, rig- orous, and inclusive. Yet, current incarnations of OA publishing seem to partly contradict these goals. In this study we investigated what we term the “APC-barrier,” finding that higher insti- tutional resourcing is associated with researchers publishing in journals with higher APCs. This linkage is nonlinear and heterogeneous across fields. Although our study has limitations regarding the measurements used, our findings suggest support for the hypothesis that author-facing charges in OA publishing present a barrier to publication and reduce the pool Quantitative Science Studies 36 The APC-barrier and its effect on stratification of knowledge that enters the scientific record. Our results extend and corroborate previous research (Olejniczak & Wilson, 2020; Siler et al., 2018; Smith et al., 2021) on potential factors that influence who is able to publish where. At a global level, we find substantial heterogeneity in average levels of APCs across fields and countries. We observe high average APCs of above U.S. $1,000 for the natural and life sciences, and low average APCs of up to U.S. $250 for the social sciences and humanities (with the exception of “Economics” and “Business”). Grouping institutions by country, we find a clear economic divide, with high average APCs for countries with a GDP per capita above U.S. $30,000, and very heterogeneous levels of average APCs for less wealthy countries. This heterogeneity can be partly attributed to the effects of policies and alternative publishing models in Latin America (Huang et al., 2020), and targeted research funding in Sub-Saharan Africa (Iyandemye & Thomas, 2019). The case of Sub-Saharan African countries is particularly interesting, as our model’s estimates of the effect of institutional resources on levels of APCs are close to zero here. This might further point towards the local importance of third-party funding in driving APC-based OA uptake, as opposed to institutional resources, which are seemingly less important. There are multiple processes at play that lead to the observed data on the distribution of APCs across fields and countries. Following Olejniczak and Wilson (2020), we jointly mod- eled the questions of whether a given publication involved an APC or not, and if yes, its mag- nitude. Our results indicate that this distinction is of relevance, given that for some fields (e.g., “Mathematics”) higher levels of institutional resourcing are associated with higher rates of zero APCs, but it is the opposite for other fields (e.g., “Environmental science,” “Sociology”). Our assumption is that institutional resources contribute to covering APCs in at least two ways: first through direct funding of APCs, and second through transformative agreements (Borrego, Anglada, & Abadal, 2021), where institutions make deals with major publishers to cover APCs. Direct funding of APCs, if not covered by transformative agreements, is often granted through institutional publishing funds, which commonly also include a cap on the maximum APC that is covered (Click & Borchardt, 2019; Solomon & Björk, 2012a). It can be assumed that such funds are more common among higher ranking institutions with greater resourcing. Considering the assumed causal pathways depicted in Figure 1, our modeling approach is able to account for some sources of confounding (country and field effects), while other poten- tial confounders have not been incorporated. One important alternative explanation for our results would be the causal path institutional resources → research quality → journal → APC amount. It can be assumed that institutional resources have some effect on research qual- ity (through better infrastructure, higher attractiveness for coauthorships, etc.). Given that there is a moderate link between the perceived quality of journals and the levels of APCs they charge (Demeter & Istratii, 2020; Gray, 2020; Maddi & Sapinho, 2022), this path could account for some of the effects we measure. However, two observations suggest that this might not be a particularly severe issue. First, the correlation between journal prestige (measured via the Impact Factor (IF)) and APC is only moderate, and further, the IF itself is highly debated as a measure for quality (Archambault & Larivière, 2009; Bar-Ilan, 2008; Larivière & Sugimoto, 2019; Lozano, Larivière, & Gingras, 2012; Waltman, 2016; Waltman & Traag, 2020). Second, our estimates of the effect of institutional resources on levels of APCs are highest for the fields “Political science” and “Sociology,” which we argue are less resource dependent than other fields with low estimates (e.g., “Physics,” “Medicine”). As pointed out by one of our reviewers, it is possible that Ptop 10% actually measures the quality of an institution rather than its resources. Our results would therefore provide an Quantitative Science Studies 37 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification estimate of the effect of quality → journal → APC amount. Although this is certainly possible, we assume Ptop 10% to be more indicative of institutional resources than of their inherent qual- ity, because it is highly correlated with overall publication output (denoted as “P” in the Leiden Ranking). This points to a crucial issue in studying the effect of institutional resources on OA publishing, which is a lack of more specific data on universities’ support budgets for APCs. Further support for initiatives like OpenAPC8 would enable this. A further alternative explanation might be that more prestigious journals with high APCs reject research from less prestigious institutions, simply because it is deemed less credible (Albornoz et al., 2020; Collyer, 2018). However, given that the negative effect of APCs on the geographic diversity of authors is substantial (Smith et al., 2021), we don’t expect this to be a major source of bias. Our analysis points to threshold effects when it comes to the effect of institutional resour- cing on levels of APCs across countries and fields. In most fields, and particularly those with more observations, the effect of the ranking position on levels of APCs is higher for lower rank- ing levels. This suggests that resources do make a difference: Once institutions reach a certain level of resourcing, they seem to be able to cover common APCs. Similarly, lower GDP is associated with a stronger effect of institutional resources on APCs in most cases (with the exceptions of Sub-Saharan Africa and, most notably, China). This again suggests that levels of resourcing play a role and points to a threshold effect: In medium- to high-income countries, most institutions can be assumed to be able to support APCs. Above this threshold, ranking differences are only weakly related to levels of APCs. In lower income countries, institutional differences are larger, and structured along the dimension of resourcing. The observed forces clearly perpetuate the system of cumulative advantage inherent to aca- demia, as well-funded research groups are better able to secure OA publications in prestigious journals with high APCs, leading to citation advantages and further funding down the line. We believe that this demonstrates the impact of APC pricing on the scholarly landscape and that these charges may have a chilling effect on opportunity and equality for researchers from less prestigious or less wealthy institutions. Such stratifications in publishing, favoring traditionally advantaged actors, will only exacerbate historical inequalities (Garuba, 2013) and undermine the wider aims of Open Science (Ross-Hellauer, 2022; Ross-Hellauer, Reichmann et al., 2022). If research is to live up to present and future challenges, it should seek to avoid modes of scholarly publication that exacerbate the marginalization of voices from societies and commu- nities less embedded in the global production of knowledge. Waivers for APCs do exist, but are, in our estimation, ineffective in countering these issues. Waivers are only applied on request, yet such discount policies are not well communicated (i.e., authors are often unaware). Hybrid journals do not usually offer waivers for OA in their journals, and where waivers are in place, often do not mitigate costs enough to encourage OA authorship (Lawson, 2015; Mekonnen, Downs et al., 2022; Rouhi, Beard, & Brundy, 2022). Most damagingly, however, in our view waivers support rather than challenge the status quo. They are a bandage on the structural inequalities exposed by a system of payment for authorship, not addressing the underlying issues but rather putting the burden on already disadvantaged scholars to appeal for assistance via poorly documented, poorly communicated, ad hoc pro- cesses whose conditions of application are apt to change. To take steps towards more equitable publishing models, a range of recommendations have been developed in the ON-MERRIT project (Cole, Reichmann, & Ross-Hellauer, 2022) and 8 https://openapc.net/. Quantitative Science Studies 38 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification beyond (UNESCO, 2021). Critically important are alternative publishing models that involve no author-facing charges at all (e.g., Diamond OA). Open and sustainable publishing infra- structures should be supported by researchers, institutions, and funders, laying the groundwork of reduced publishing costs and shaping a future where publishing involves no charges, neither for authors nor for readers. In parallel, self-archiving of peer-reviewed works should continue to receive increased attention, given that it can immediately be realized by researchers and institutions. Despite the worrying trends observed in our study, solutions are available that promise to move scholarly communication towards more equity and diversity. 4.1. Limitations and Future Directions Assembling the data set for this study involved many steps and decisions that could potentially threaten the validity of our results. First, inclusion of universities into the Leiden Ranking can be understood as a marker for resources in itself, as high production of internationally recog- nized research is a precondition. It is therefore fair to assume that the adverse effect of APC levels on the inclusivity and equity of the scholarly publishing landscape is even stronger for researchers from less resourced institutions. Second, the Leiden Ranking offers a wide range of indicators, and we analyze only one of multiple potential proxy indicators for institutional resources. Third, the definitions of institutions (e.g., which institutes/hospitals/etc. to include) might not completely overlap between OpenAlex and the Leiden Ranking. Additionally, affil- iation data from OpenAlex is less complete and reliable for smaller publishers, which might bias our results9. Fourth, our analysis uses static values for APCs. Although this is a common approach in the literature (e.g., Zhang et al., 2022), it might introduce uncertainty in the estimates or even bias them, given that journals do in fact change their APCs (Asai, 2020, 2021; Morrison, Salhab et al., 2015). Butler et al. (2022) conducted a similar study to Zhang et al. (2022) but used annual price lists and historical data on APCs to yield more accurate values for actual APCs paid. Their estimate for the revenue of the five largest publishers is hence lower than the esti- mate of Zhang and colleagues. Because we used the same approach as Zhang et al., it is likely that our analysis also somewhat overestimates the averages for APCs. However, our estimates for the relationship between institutional resources and APCs would then only be biased to the extent that recent price increases differed greatly across disciplines or geographic regions. We leave this as an avenue for future research. Fifth, we restricted our analysis to publications in fully OA journals, not considering hybrid OA publications. Given that APC charges for hybrid publications are generally higher than for gold OA publications, we assume that the observed trends would be even stronger for OA publications in hybrid journals. Finally, the modeling approach taken to disentangle field and country effects posed computational challenges. We have followed available best prac- tices, but the conclusions should be treated as explorative and be backed up by future research with potentially different approaches. Our study opens up multiple avenues for further research. Of primary concern should be to find more direct measures of institutional resources. Although we assume that our proxy works well, more direct measures, such as data on institutional support for APCs, general library sup- port on OA, etc., should be considered. Our analysis incorporated the dimension of time for the descriptive results; a more stringent treatment within a suitable model could shed further 9 A point made by Ludo Waltman in personal communication. Quantitative Science Studies 39 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification light on how the association between institutional resources and levels of APCs changes over time. Lastly, replication attempts using different data sources for bibliographic data (e.g., Web of Science, Scopus, Dimensions) or data on APCs (e.g., OpenAPC) could provide further support for the presented conclusions. ACKNOWLEDGMENTS The authors thank Juan Pablo Alperin, Christian Fleck, Vincent Traag, and Ludo Waltman for comments on an earlier version of the manuscript, as well as Benjamin Klebel-Knobloch for fruitful discussions on the modeling strategy. AUTHOR CONTRIBUTIONS Thomas Klebel: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing—original draft, Writing—review & editing. Tony Ross-Hellauer: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing—review & editing. COMPETING INTERESTS The authors have no competing interests. FUNDING INFORMATION The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement number 824612, and from the European Union’s Horizon Europe Programme under Grant Agreement number 101058728. The views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. The Know-Center is funded within COMET—Competence Centers for Excellent Technologies—under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry of Economy, Family and Youth and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG. DATA AVAILABILITY The data sets created in the course of this analysis are available from Zenodo (Klebel & Ross- Hellauer, 2022b). All code to reproduce the creation and analysis of the data is available from Zenodo (Klebel & Ross-Hellauer, 2022a). This includes saved objects from brms, which enables analyzing the model without the need to run the sampler. REFERENCES Albornoz, D., Huang, M., Martin, I. M., Mateus, M., Touré, A. Y., & Chan, L. (2018). Framing power: Tracing key discourses in open science policies. In 22nd International Conference on Electronic Publishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, ELPUB 2018. Toronto. https://doi .org/10.4000/proceedings.elpub.2018.23 Albornoz, D., Okune, A., & Chan, L. (2020). Can open scholarly practices redress epistemic injustice? In M. P. Eve & J. Gray (Eds.), Reassembling scholarly communications: Histories, infrastructures, and global politics of open access. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/11885.003 .0009 Archambault, É., & Larivière, V. (2009). History of the journal impact factor: Contingencies and consequences. Scientometrics, 79(3), 635–649. https://doi.org/10.1007/s11192-007-2036-x Arel-Bundock, V. (2022). WDI: World Development Indicators and other World Bank data (v2.7.6). https://CRAN.R-project.org /package=WDI Quantitative Science Studies 40 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification Asai, S. (2020). Determinants of revisions to article processing charges for BMC journals. Publishing Research Quarterly, 36(1), 63–73. https://doi.org/10.1007/s12109-019-09677-1 Asai, S. (2021). An analysis of revising article processing charges for open access journals between 2018 and 2020. Learned Publish- ing, 34(2), 137–143. https://doi.org/10.1002/leap.1334 Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century—A review. Journal of Informetrics, 2(1), 1–52. https:// doi.org/10.1016/j.joi.2007.11.001 Beery, A. K., & Zucker, I. (2011). Sex bias in neuroscience and biomedical research. Neuroscience and Biobehavioral Reviews, 35(3), 565–572. https://doi.org/10.1016/j.neubiorev.2010.07 .002, PubMed: 20620164 Björk, B.-C., & Solomon, D. (2015). Article processing charges in OA journals: Relationship between price and quality. Scientometrics, 103(2), 373–385. https://doi.org/10.1007/s11192-015-1556-z Borrego, Á., Anglada, L., & Abadal, E. (2021). Transformative agreements: Do they pave the way to open access? Learned Publishing, 34(2), 216–232. https://doi.org/10.1002/leap.1347 Burchardt, J. (2014). Researchers outside APC-financed Open Access: Implications for scholars without a paying institution. SAGE Open, 4(4), 2158244014551714. https://doi.org/10.1177 /2158244014551714 Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2022). The oligopoly’s shift to open access. How for-profit publishers benefit from article processing charges. Zenodo. https://doi.org/10.5281/zenodo.7057144 C a b r e r i z o , F. M . ( 2 0 2 2 ) . O p e n a c c e s s i n l o w - i n c o m e countries—Open letter on equity. Nature, 605(7911), 620. https://doi.org/10.1038/d41586-022-01414-7, PubMed: 35610369 Chan, L., Cuplinskas, D., Eisen, M., Friend, F., Genova, Y., … Velterop, J. (2002). Budapest Open Access Initiative. https:// w e b . a r c h i v e . o r g / w e b / 2 0 1 7 1 2 1 3 0 9 3 7 0 8 / h t t p s : / / w w w .budapestopenaccessinitiative.org/read Click, A. B., & Borchardt, R. (2019). Library supported open access funds: Criteria, impact, and viability. Evidence Based Library and Information Practice, 14(4), 21–37. https://doi.org/10.18438 /eblip29623 Cole, N. L., Reichmann, S., & Ross-Hellauer, T. (2022). Global thinking. ON-MERRIT recommendations for maximising equity in open and responsible research. Zenodo. https://doi.org/10 .5281/zenodo.6276753 Collyer, F. M. (2018). Global patterns in the publishing of academic knowledge: Global North, Global South. Current Sociology, 66(1), 56–73. https://doi.org/10.1177/0011392116680020 Czerniewicz, L. (2015). Opinion: Confronting inequitable power dynamics of global knowledge production and exchange. Water Wheel, 14(5), 26–29. Deardorff, J., Hoyt, L. T., Carter, R., & Shirtcliff, E. A. (2019). Next steps in puberty research: Broadening the lens toward understu- died populations. Journal of Research on Adolescence, 29(1), 133–154. https://doi.org/10.1111/jora.12402, PubMed: 30869847 Demeter, M., & Istratii, R. (2020). Scrutinising what Open Access journals mean for global inequalities. Publishing Research Quarterly, 36, 505–522. https://doi.org/10.1007/s12109-020 -09771-9 Ellers, J., Crowther, T. W., & Harvey, J. A. (2017). Gold open access publishing in mega-journals: Developing countries pay the price of western premium academic output. Journal of Scholarly Publishing, 49(1), 89–102. https://doi.org/10.3138/jsp .49.1.89 ElSabry, E. (2017). Unaffiliated researchers: A preliminary study. Challenges, 8(2), 20. https://doi.org/10.3390/challe8020020 Eve, M. P. (2014). Open access and the humanities. Cambridge: C a m b r i d g e U n i v e r s i t y P r e s s . h t t p s : / / d o i . o rg / 1 0 . 1 0 1 7 /CBO9781316161012 Frenken, K., Heimeriks, G. J., & Hoekman, J. (2017). What drives university research performance? An analysis using the CWTS Leiden Ranking data. Journal of Informetrics, 11(3), 859–872. https://doi.org/10.1016/j.joi.2017.06.006 Garuba, A. R. (2013). The prospects of bridging the digital divide in Africa. Library Philosophy and Practice, 2013, 882. https://www .scopus.com/inward/record.uri?eid=2-s2.0-85085323161 &partnerID=40&md5=530601d0b82a6c389c300e61b548f11a Gelman, A., & Hill, J. (2009). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge Univer- sity Press. Gomez, C. J., Herman, A. C., & Parigi, P. (2022). Leading countries in global science increasingly receive more citations than other countries doing similar research. Nature Human Behaviour, 6(7), 919–929. https://doi.org/10.1038/s41562-022-01351-5, PubMed: 35637294 Gray, R. J. (2020). Sorry, we’re open: Golden open-access and inequality in non-human biological sciences. Scientometrics, 124(2), 1663–1675. https://doi.org/10.1007/s11192-020-03540-3 Huang, C.-K. (Karl), Neylon, C., Hosking, R., Montgomery, L., Wilson, K. S., … Brookes-Kenworthy, C. (2020). Evaluating the impact of open access policies on research institutions. eLife, 9, e57067. https://doi.org/10.7554/eLife.57067, PubMed: 32924933 Iyandemye, J., & Thomas, M. P. (2019). Low income countries have the highest percentages of open access publication: A systematic computational analysis of the biomedical literature. PLOS ONE, 14(7), e0220229. https://doi.org/10.1371/journal.pone.0220229, PubMed: 31356618 Klebel, T., & Ross-Hellauer, T. (2022a). Code for “The APC-Barrier and its effect on stratification in open access publishing”. Zenodo. https://doi.org/10.5281/zenodo.7198844 Klebel, T., & Ross-Hellauer, T. (2022b). Data for “The APC-Barrier and its effect on stratification in open access publishing” [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7014802 Larivière, V., Desrochers, N., Macaluso, B., Mongeon, P., Paul-Hus, A., & Sugimoto, C. R. (2016). Contributorship and division of labor in knowledge production. Social Studies of Science, 46(3), 417–435. https://doi.org/10.1177/0306312716650046, PubMed: 28948891 Larivière, V., & Sugimoto, C. R. (2019). The Journal Impact Factor: A brief history, critique, and discussion of adverse effects. In W. Glänzel, H. F. Moed, U. Schmoch, & M. Thelwall (Eds.), Springer handbook of science and technology indicators (pp. 3–24). Cham: Springer International Publishing. https://doi.org/10.1007 /978-3-030-02511-3_1 Lawson, S. (2015). Fee waivers for open access journals. Publications, 3(3), 155–167. https://doi.org/10.3390/publications3030155 Lozano, G. A., Larivière, V., & Gingras, Y. (2012). The weakening relationship between the impact factor and papers’ citations in the digital age. Journal of the American Society for Information Science and Technology, 63(11), 2140–2145. https://doi.org/10 .1002/asi.22731 Luraschi, J., Kuo, K., Ushey, K., Allaire, J. J., Falaki, H., … The Apache Software Foundation. (2022). sparklyr: R Interface to Apache Spark (v1.7.5). https://CRAN.R-project.org/package=sparklyr Maddi, A., & Sapinho, D. (2022). Article processing charges, altmetrics and citation impact: Is there an economic rationale? Quantitative Science Studies 41 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification Scientometrics, 127, 7351–7368. https://doi.org/10.1007/s11192 -022-04284-y Mallapaty, S. (2020). China bans cash rewards for publishing papers. Nature, 579(7797), 18. https://doi.org/10.1038/d41586 -020-00574-8, PubMed: 32127708 Matheka, D. M., Nderitu, J., Mutonga, D., Otiti, M. I., Siegel, K., & Demaio, A. R. (2014). Open access: Academic publishing and its implications for knowledge equity in Kenya. Globalization and Health, 10, 26. https://doi.org/10.1186/1744-8603-10-26, PubMed: 24716579 McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.). Taylor & Francis, CRC Press. https://doi.org/10.1201/9780429029608 Mekonnen, A., Downs, C., Effiom, E. O., Kibaja, M., Lawes, M. J., … Chapman, C. A. (2022). Can I afford to publish? A dilemma for African scholars. Ecology Letters, 25(4), 711–715. https://doi.org /10.1111/ele.13949, PubMed: 34957647 Miao, L., Murray, D., Jung, W.-S., Larivière, V., Sugimoto, C. R., & Ahn, Y.-Y. (2022). The latent structure of global scientific devel- opment. Nature Human Behaviour, 6, 1206–1217. https://doi.org /10.1038/s41562-022-01367-x, PubMed: 35654964 Microsoft. (2021). Next steps for Microsoft Academic—Expanding into new horizons. Microsoft Research. https://www.microsoft.com/en -us/research/project/academic/articles/microsoft-academic-to -expand-horizons-with-community-driven-approach/ Momeni, F., Dietze, S., Mayr, P., Biesenbender, K., & Peters, I. (2022). Which factors drive open access publishing? A Springer Nature case study. arXiv, arXiv:2208.08221. https://doi.org/10 .48550/arXiv.2208.08221 Morrison, H., Salhab, J., Calvé-Genest, A., & Horava, T. (2015). Open access article processing charges: DOAJ survey May 2014. Publications, 3(1), 1–16. https://doi.org/10.3390 /publications3010001 Naik, C., Sugimoto, C. R., Larivière, V., Leng, C., & Guo, W. (2022). Impact of geographic diversity on citation of collaborative research. arXiv, arXiv:2204.11713. https://doi.org/10.48550 /arXiv.2204.11713 Niles, M. T., Schimanski, L. A., McKiernan, E. C., & Alperin, J. P. (2020). Why we publish where we do: Faculty publishing values and their relationship to review, promotion and tenure expecta- tions. PLOS ONE, 15(3), e0228914. https://doi.org/10.1371 /journal.pone.0228914, PubMed: 32160238 Olejniczak, A. J., & Wilson, M. J. (2020). Who’s writing open access (OA) articles? Characteristics of OA authors at Ph.D.- granting institutions in the United States. Quantitative Science Studies, 1(4), 1429–1450. https://doi.org/10.1162/qss_a_00091 Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. Chichester: Wiley. Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., … Haustein, S. (2018). The state of OA: A large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375. https://doi.org/10.7717/peerj.4375, PubMed: 29456894 Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and con- cepts. arXiv, arXiv:2205.01833. https://doi.org/10.48550/arXiv .2205.01833 Rohrer, J. M. (2018). Thinking clearly about correlations and cau- sation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629 Ross-Hellauer, T. (2022). Open science, done wrong, will com- pound inequities. Nature, 603(7901), 363. https://doi.org/10 .1038/d41586-022-00724-0, PubMed: 35288691 Ross-Hellauer, T., Reichmann, S., Cole, N. L., Fessl, A., Klebel, T., & Pontika, N. (2022). Dynamics of cumulative advantage and threats to equity in open science: A scoping review. Royal Society Open Science, 9(1), 211032. https://doi.org/10.1098 /rsos.211032, PubMed: 35116143 Rouhi, S., Beard, R., & Brundy, C. (2022). Left in the cold: The fail- ure of APC waiver programs to provide author equity. Science Editor, 45, 5–13. https://doi.org/10.36591/SE-D-4501-5 Scheidsteger, T., & Haunschild, R. (2022). Comparison of metadata with relevance for bibliometrics between Microsoft Academic Graph and OpenAlex until 2020. arXiv, arXiv:2206.14168. https://doi.org/10.48550/arXiv.2206.14168 Segado-Boj, F., Martín-Quevedo, J., & Prieto-Gutiérrez, J. J. (2018). Attitudes toward open access, open peer review, and altmetrics among contributors to Spanish scholarly journals. Journal of Scholarly Publishing, 50(1), 48–70. https://doi.org/10.3138/jsp .50.1.08 Siler, K., & Frenken, K. (2019). The pricing of open access journals: Diverse niches and sources of value in academic publishing. Quantitative Science Studies, 1(1), 28–59. https://doi.org/10 .1162/qss_a_00016 Siler, K., Haustein, S., Smith, E., Larivière, V., & Alperin, J. P. (2018). Authorial and institutional stratification in open access publish- ing: The case of global health research. PeerJ, 6, e4269. https:// doi.org/10.7717/peerj.4269, PubMed: 29479492 Simard, M.-A., Ghiasi, G., Mongeon, P., & Larivière, V. (2022). National differences in dissemination and use of open access literature. PLOS ONE, 17(8), e0272730. https://doi.org/10.1371 /journal.pone.0272730, PubMed: 35943972 Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., … Wang, K. (2015). An overview of Microsoft Academic Service (MAS) and applica- tions. In Proceedings of the 24th International Conference on World Wide Web—WWW ’15 Companion (pp. 243–246). https://doi.org/10.1145/2740908.2742839 Smith, A. C., Merz, L., Borden, J. B., Gulick, C. K., Kshirsagar, A. R., & Bruna, E. M. (2021). Assessing the effect of article processing charges on the geographic diversity of authors using Elsevier’s “Mirror Journal” system. Quantitative Science Studies, 2(4), 1123–1143. https://doi.org/10.1162/qss_a_00157 Solomon, D. J., & Björk, B.-C. (2012a). Publication fees in open access publishing: Sources of funding and factors influencing choice of journal. Journal of the American Society for Information Science and Technology, 63(1), 98–107. https://doi.org/10.1002/asi.21660 Solomon, D. J., & Björk, B.-C. (2012b). A study of open access journals using article processing charges. Journal of the American Society for Information Science and Technology, 63(8), 1485–1495. https://doi.org/10.1002/asi.22673 Stehr, N. (1994). Knowledge societies. London: Sage. Turner, B. E., Steinberg, J. R., Weeks, B. T., Rodriguez, F., & Cullen, M. R. (2022). Race/ethnicity reporting and representation in U.S. clinical trials: A cohort study. The Lancet Regional Health – Americas, 11, 100252. https://doi.org/10.1016/j.lana.2022 .100252, PubMed: 35875251 UNESCO. (2021). UNESCO recommendation on open science. https://en.unesco.org/science-sustainable-future/open-science /recommendation Van Eck, N. J. (2021). CWTS Leiden Ranking 2021 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4889279 Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391. https://doi .org/10.1016/j.joi.2016.02.007 Waltman, L., & Traag, V. A. (2020). Use of the journal impact factor for assessing individual articles need not be statistically wrong. Quantitative Science Studies 42 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The APC-barrier and its effect on stratification F1000Research, 9, 366. https://doi.org/10.12688/f1000research .23418.1 Xie, Y., Zhang, C., & Lai, Q. (2014). China’s rise as a major contrib- utor to science and technology. Proceedings of the National Academy of Sciences, 111(26), 9437–9442. https://doi.org/10 .1073/pnas.1407709111, PubMed: 24979796 Young, K., Fisher, J., & Kirkman, M. (2019). “Do mad people get endo or does endo make you mad?”: Clinicians’ discursive con- structions of medicine and women with endometriosis. Feminism & Psychology, 29(3), 337–356. https://doi.org/10.1177 /0959353518815704 Zhang, L., Wei, Y., Huang, Y., & Sivertsen, G. (2022). Should open access lead to closed research? The trends towards paying to per- form research. Scientometrics, 127, 7653–7679. https://doi.org /10.1007/s11192-022-04407-5 Zhou, P., & Leydesdorff, L. (2006). The emergence of China as a leading nation in science. Research Policy, 35(1), 83–104. https://doi.org/10.1016/j.respol.2005.08.006 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u q s s / a r t i c e - p d l f / / / / / 4 1 2 2 2 0 7 8 3 7 9 q s s _ a _ 0 0 2 4 5 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Quantitative Science Studies 43

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