Bridging the Gap: Best Practices for OA Journals Articulating Policies for Open Repository Archiving
Bibliographic record
Abstract
Although it may seem an implicit truth that open access journals exchange their content easily with open repositories (institutional or disciplinary), in practice, those who manage open repositories often have difficulty locating, identifying and interpreting the sharing policies of open access journals. This results in blockers and inefficiencies in bringing OA content into open repositories - which are environments can significantly increase the visibility of openly accessible research. Recent research by Schlosser (Schlosser, M., (2016). Write up! A Study of Copyright Information on Library-Published Journals. Journal of Librarianship and Scholarly Communication. 4, p.eP2110) revealed that 76% of journals in an analyzed sample in a mostly-OA set of journals did not have clear copyright articulation or sharing policies. This session will make recommendations to journal editors about articulating open archiving policies on journal websites. Furthermore, this session will also suggest strategies for institutional repository managers and open journal systems/library publishing managers at academic libraries to collaborate to inform journal editorial boards about the existence of open repositories, and help promote best practices for sharing content with these sites of open research discovery.
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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 | 0.506 | 0.566 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.026 | 0.030 |
| Scholarly communication | 0.082 | 0.085 |
| Open science | 0.018 | 0.033 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".