Love Data: Persistent Identifiers in Scholarly Communication Networks - the ORCID Advantage
Bibliographic record
Abstract
This webinar was recorded via Zoom on Wednesday, February 5, 2025.Persistent Identifiers or PIDs, are valuable pieces of information assigned to authors, articles, journals, books, funders, institutions, and other discrete elements embedded in scholarly communication or academic networks. PIDs are unique, open and interoperable, which makes them ideal to connect and distribute associated metadata seamlessly through the ecosystem. Join us to learn about the myriad ways you can use PIDs, such as ORCID, to promote your work and connect with others in academia.Mike Nason is the Open Scholarship and Publishing Librarian at the University of New Brunswick in Fredericton, New Brunswick, Canada. He is also the Metadata and Crossref Liaison with the Public Knowledge Project (PKP) and a PKP Publishing Services team member. He currently serves on the Coalition Publica technical committee and was the chair of their metadata working group, a two-year project to establish better metadata practices for the Coalition Publica membership and broader OJS community. Mike also chaired the ORCID-CA governing committee as part of the Canadian Research Knowledge Network (CRKN) consortium.This workshop is part of Love Data Month, run by the Data Services Team. This specific event is sponsored by the Open Scholarship Community Rochester (OSCR).
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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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.023 | 0.045 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.168 | 0.083 |
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".