Towards a Holistic Approach to Policy Interoperability in Digital Libraries and Digital Repositories
Bibliographic record
Abstract
Underpinning every digital library and digital repository there is a policy framework, which makes the digital library viable - without a policy framework a digital library is little more than a container for content. Policy governs how a digital library is instantiated and run. It is therefore a meta-domain which is situated both outside the digital library and any technologies used to deliver it, and within the digital library itself. Policy is also a key aspect of digital library and digital repository interoperability in a common and integrated information space. Policy interoperability - that is the exchange and reuse of policies - is a step beyond policy standardisation. Furthermore, effective and efficient policy frameworks are also one of the Digital Curation Center (DCC), DigitalPreservationEurope (DPE), nestor and Center for Research Libraries (CRL) core criteria for digital repositories. In this article, we share our research on policy interoperability levels and the experimental survey on policy interoperability conducted with real-life digital libraries, as a contribution towards the definition of a Policy Interoperability Framework.
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 | 0.054 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.033 | 0.047 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".