Transitioning to open access (OA)
Bibliographic record
Abstract
This paper presents a summary of three presentations: Heather Joseph of the Scholarly Publishing and Academic Resources Coalition (SPARC) on key advocacy strategies, the Canadian Association of Research Libraries’s (CARL) Kathleen Shearer on the CARL Institutional Repository program and forthcoming CARL Author’s Addendum, and Heather Morrison on the Canadian Library Association’s (CLA) Task Force on Open Access. The presentations were followed by a one–hour workshop, with about 50 participants including librarians from Canada and elsewhere, publishers, and others. Workshop exercises, designed for the expert audience anticipated at the First International PKP Scholarly Publishing Conference, were developed to elicit a broad overview of open access initiatives underway, issues and barriers to open access, and solutions to overcome them. Participants reported being engaged in a wide variety of open access initiatives, from OA publishing and institutional repositories to a recent commitment to devote a percentage of a university budget to OA. Two solutions the workshop participants saw as key for open access were finding a funding solution (possibly re–deploying collections and acquisitions budgets or earmarking grants funds for knowledge transfer), and branding repositories as containing trustable material. The workshop portion could have been expanded considerably, to a half or full day. Results of the workshop will help to inform the work of the CLA Task Force on Open Access.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".