Rights Retention: A Tool for Open Access
Bibliographic record
Abstract
Information Services and Research Services work together on many initiatives to support the research community. As well as ensuring the specialist knowledge of each of our Services is applied to the task, this fosters the sharing of information, experience and skills. The University is keen to support researchers in making their work publicly available. This poster describes how we worked together with colleagues across UofG to launch a new ‘Research Publications and Copyright Policy’ centred on a rights retention approach. Rights retention is a growing trend that enables authors to exercise their rights to deposit an author-accepted manuscript (AAM) in a repository and provide open access to it. It also supports authors where research funders and future research assessment exercises mandate open access. The policy went live on 1 September 2023 and there was considerable learning during the process. Colleagues stepped up to learn about new topics, grapple with legalities and consult with key stakeholders. We also delivered a communications plan that involved briefing senior management, committees and communities and providing coherent materials to support the new policy. The policy will be reviewed after a year. This poster sets out our main achievements so far, what we’ve learned from the process and where we are heading next.
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 | 0.105 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.022 | 0.055 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.142 | 0.085 |
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".