A Very Long Embargo: Journal Choice Reveals Active Non-Compliance with Funder Open Access Policies by Australian and Canadian Neuroscientists
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometricsOpen science Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | MetaresearchBibliometricsScholarly communicationOpen science Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.335 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.039 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".