Persistent Identifiers in Canada: ORCID Use Cases and a National PID Strategy
Bibliographic record
Abstract
Research generates a huge amount of information across many disconnected systems and technologies. Persistent \nIdentifiers (PIDs) act as labels to uniquely identify research information ‘entities,’ like scholars, institutions, \ndatasets, and publications. PIDs are anchors that help connect information about related entities (e.g., a scholar \nwith their publications) and can enable software systems to effectively exchange information, making them more \ninteroperable and FAIR. The gold standard PID for People is the ORCID iD provided by ORCID, an international \nnot-for-profit sustained by institutional membership. In Canada, members are supported by the local consortium, \nORCID-CA, in both English and French. \nIn this session, first, we will explore what ORCID iDs are and, why they matter. We will place ORCID iDs within the \nbroader PID ecosystem context, and then highlight the value of specific ORCID member tools, such as the Affiliation \nManager (which enables institutions to add trusted affiliation information on behalf of their scholars, with scholar \npermission) and the Affiliation Report (a tool to measure ORCID impact and uptake at a given institution). Then, we \nwill explore a community use case to demonstrate ORCID’s value and the usefulness of PIDs in assessing research \nimpact. Finally, an update will be provided on the state of the development of a National PID Strategy for Canada, \nwhich was last discussed at BRIC 2022. Significant advancements have been made and a Roadmap to (PID) Success \n(community recommendations based on work to date) will be presented.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".