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Record W4291982736

A Very Long Embargo: Journal Choice Reveals Active Non-Compliance with Funder Open Access Policies by Australian and Canadian Neuroscientists

2018· article· en· W4291982736 on OpenAlexaffabout
Shaun Yon‐Seng Khoo, Belinda P. P. Lay

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2018
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsConcordia University
Fundersnot available
KeywordsCompliance (psychology)Political sciencePublic economicsPsychologyPublic administrationPublic relationsEconomicsSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Research funders around the world have implemented open access policies that require funded research to be made open access, usually by self-archiving, within 12 months of publication. Elsevier is unique among major science publishers because it produces several journals with non-compliant self-archiving embargoes of more than 12 months. We used Elsevier’s Scopus database to study the rate at which Australian and Canadian neuroscientists publish in Elsevier’s non-compliant (embargoes > 12 months) and compliant journals (embargoes ≤ 12 months). We also examined publications in immediate open access neuroscience journals that had the DOAJ Seal and neuroscience publications in open access mega-journals. We found that the implementation of Australian and Canadian funder open access policies in 2012/2013 and 2015 did not reduce the number of publications in non-compliant journals. Instead, scientific output in all publication types increased with the greatest growth in immediate open access journals. This data suggests that funder open access policies that are similar to the Australian and Canadian policies are likely to have little effect beyond an association with a general cultural trend towards open access.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometricsOpen science
Domain: Incentives · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Observationallow
gptMetaresearchBibliometricsScholarly communicationOpen science
Domain: Incentives · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.037
metaresearch head score (Gemma)0.335
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.335
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0140.039
Science and technology studies0.0050.004
Scholarly communication0.0090.005
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.451
GPT teacher head0.526
Teacher spread0.075 · 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

Labeled directly by 2 models reading the full record.

MetaresearchBibliometricsOpen scienceScholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
DomainIncentives
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

Citations1
Published2018
Admission routes2
Has abstractyes

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