MétaCan
Menu
Back to cohort
Record W4290798458 · doi:10.5281/zenodo.6975589

Geographic differences in the uptake of diamond open access and APCs

2022· paratext· en· W4290798458 on OpenAlexaff
Marc‐André Simard, Isabel Basson, Vincent Larivière

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typeparatext
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDiamondComputer scienceMaterials science

Abstract

fetched live from OpenAlex

Geographical and income level analyses have shown that gold OA journals tend to be much more expensive in the Western world and in richer countries compared to other regions where APCs are generally much cheaper and skewed by a minority of expensive journals (Figure 4). Despite that, APCs often dominate the conversation regarding the sustainability and affordability of OA publishing. However, this ignores the funding models that the vast majority of DOAJ listed journals (71%) are using as they do not charge APCs. These diamond journals have also increased at a higher rate than their APC-based counterparts. Differences in the adoption of diamond OA may be observed between regions with Latin America achieving close to 100% diamond OA through regional initiatives such as the SciELO platform. While problems related to the perceived quality of diamond journals persist among researchers, our results shows that the Western APC-based way to do OA does not have to be the only way for researchers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Open science
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.172
GPT teacher head0.358
Teacher spread0.187 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicResearch Data Management PracticesFrench-language works237,207