Licensing Revisited: Open Access Clauses in Practice
Bibliographic record
Abstract
Open access increases the visibility and use of research outputs and promises to maximize the return on our public investment in research. However, only a minority of researchers will "spontaneously" deposit their articles into an open access repository. Even with the growing number of institutional and funding agency mandates requiring the deposit of papers into the university repository, deposit rates have remained stubbornly low. As a result, the responsibility for populating repositories often falls onto the shoulders of library staff and/or repository managers. Populating repositories in this way – which involves obtaining the articles, checking the rights, and depositing articles into the repository – is time consuming and resource intensive work.The Confederation of Open Access Repositories (COAR), a global association of repository initiatives and networks, is promoting a new strategy for addressing some of the barriers to populating repositories, involving the use of open access archiving clauses in publisher licenses. These types of clauses are being considered by consortia and licensing agencies around the world as a way of ensuring that all the papers published by a given publisher are cleared for deposit into the institutional repository. This paper presents some use cases of open access archiving clauses, discusses the major barriers to implementing archiving language into licenses, and describes some strategies that organizations can adopt in order to include such clauses into publisher licenses.
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
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 | 0.131 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.037 | 0.071 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 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".