IMPLEMENTING OPEN ACCESS: REPORT OF THE CARL-CRKN OPEN ACCESS WORKING GROUP
Bibliographic record
Abstract
Open Access (OA) is a movement to provide unrestricted access to the results of research and scholarship and had its initial beginnings in the early nineties in the scientific research community, partly inspired by the growth of the Internet and changes in information technology. Recent developments include a growing momentum worldwide to establish national OA policies. The stage has now been reached where the dialogue about public access to research output is about how to implement OA, not whether it should advance. In this report, the Open Access Working Group (OAWG), jointly created by CARL and CRKN, has focussed on what can be done to advance OA in the context of Canadian research and scholarly publishing, at the same time acknowledging the varying interests of the member organizations of CARL libraries and CRKN institutions. This report also challenges CARL and CRKN to continue their ground-breaking organizational collaboration to advance OA, begun with this working group. Academic libraries have clearly demonstrated their support for OA publishing and have been among its major champions. Many libraries have ceased to be consumers, and have moved actively into production support roles for OA publishing by offering a range of scholarly communication services – OA author funds, OA initiative sponsors, local repositories, journal hosting, and other support. Research and
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How this classification was reachedexpand
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 | 0.124 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".