Checklist: Pathways to National PID Strategies
Bibliographic record
Abstract
The RDA National PID Strategies Working Group was endorsed to explore how Persistent Identifiers (PIDs) form part of national policy and research infrastructure implementation frameworks. The Group recognises that there are systemic and network benefits from widespread and consistent PID adoption including financial and time savings benefits. Research sector stakeholders including funders, government agencies, and national research communities have created PID consortia or policies (including mandates) in pursuit of these benefits. At the establishment of the WG, National PID Strategies were beginning to emerge in the UK, Australia, the Netherlands, and Canada as a pathway to realising these benefits and an international conversation felt needed. RDA provided an umbrella for discussion and alignment between the strategies, refinement of the value proposition and sharing practical development pathways to a national PID strategy. The group produced a Guide that compares and contrasts national PID strategies based on nine case studies they collected collected. The Guide included a Checklist to developing a national PID strategy. This poster provides a visual representation of the Checklist and was developed as part of RDA TIGER (EC GA 101094406) support to the National PID Strategies Working Group's engagement activities.
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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 | 0.072 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.023 |
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".