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Record W4295292813 · doi:10.1590/scielopreprints.4729

Recalibrating the Scope of Scholarly Publishing: A Modest Step in a Vast Decolonization Process

2022· preprint· en· W4295292813 on OpenAlexaff
Saurabh Khanna, Jon Ball, Juan Pablo Alperín, John Willinsky

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsScopusPublishingPortugueseScope (computer science)Library sciencePolitical scienceIndonesianDiversity (politics)Scholarly communicationWorld Wide WebSocial scienceMedia studiesHistorySociologyComputer scienceLawLinguisticsMEDLINE

Abstract

fetched live from OpenAlex

By analyzing 25,671 journals largely absent from common journal counts, as well as Web of Science and Scopus, this study demonstrates that scholarly communication is more of a global endeavor than is commonly credited. These journals, employing the open source publishing platform Open Journal Systems (OJS), have published 5.8 million items; they are in 136 countries, with 79.9% in the Global South and 84.2% following the OA diamond model (charging neither reader nor author). A substantial proportion of journals operate in more than one language (48.3%), with research published in a total of 60 languages (led by English, Indonesian, Spanish, and Portuguese). The journals are distributed across the social sciences (45.9%), STEM (40.3%), and the humanities (13.8%). For all their geographic, linguistic, and disciplinary diversity, 1.2% are indexed in the Web of Science and 5.7% in Scopus. On the other hand, 1.0% are found in Cabells Predatory Reports, while 1.4% show up in Beall’s questionable list. This paper seeks to both contribute and historically situate expanded scale and diversity of scholarly publishing in the hope that this recognition may assist humankind in taking full advantage of what is increasingly a global research enterprise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.070
metaresearch head score (Gemma)0.209
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0700.209
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0680.280
Science and technology studies0.0000.000
Scholarly communication0.0480.008
Open science0.0100.009
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.636
GPT teacher head0.577
Teacher spread0.059 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2022
Admission routes1
Has abstractyes

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