ORCID AND OPENAIRE COMPLIANCE FOR DSPACE: OR2021
Bibliographic record
Abstract
Members of the Canadian Association of Research Libraries’ Open Repositories Working Group (CARL-ORWG) have a common goal of making research outcomes generated at their universities openly available to the global knowledge commons. In 2018 a subset of CARL-ORWG led by Queen’s University pooled their resources and hired 4Science to develop code to make aggregation from DSpace current versions (5 & 6) into OpenAire possible. In discussions with 4Science it was proposed and decided that this development work include a patch for adding ORCIDs to the required OAI-PMH feed. This presentation will provide background on this completed work including the principles and goals for open research shared by CARL members and 4Science and the details of the ORCID patch. DSpace is the most popular open source repository platform in the world and this implementation will bring benefit to the vast global community using the latest versions of DSpace, besides providing guidance and inspiration to other communities.
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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 | 0.097 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.188 | 0.099 |
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".