Revisiting the importance of persistence in the scholarly web
Bibliographic record
Abstract
The proliferation of digital libraries and repositories at research institutions over several decades has transformed how research is performed and disseminated. The increasing openness of these research collections makes it possible for more people to access digital research assets, and this access supports increasingly open research with all its benefits. However, although the importance of stable addressing has been well understood for more than a quarter of a century, it has gradually become apparent that there is a crisis in the persistence of resources in the scholarly web. Even though university libraries often purport to demonstrate exemplary digital collection management, this is not always being achieved in practice. In fact, there is a growing body of evidence that libraries are especially irresponsible in their digital collection management. Far from helping to solve the persistence crisis, many universities instead appear to be undermining integrity in the scholarly record and disrupting content discovery, research citation, verification, and reproducibility — all fundamental to scholarship itself. This paper revisits the principles of persistence and the concept of 'Cool URIs', as introduced by Tim Berners-Lee, and explores data on open repository persistence within the UK. The paper will also discuss the possible causes for the repository persistence challenge and possible solutions.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.029 | 0.037 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".