Bibliographic record
Abstract
Purpose This paper aims to provide a case study of an ORCID promotion at the University of Waterloo School of Optometry and Vision Science, providing context for the importance of education in ORCID outreach. Design/methodology/approach The three-month ORCID promotion used workshops and individual appointments to educate faculty about ORCID, identity management systems and research impact and scholarly communications. Findings A targeted and personal approach to ORCID promotion focused on education about why you might use this author disambiguation system resulted in 80% of the faculty within the School of Optometry and Vision Science signing up for, or using ORCID. Scaling an ORCID implementation to a larger group would likely benefit from a dedicated project group, and integration with existing institutional systems such as a requirement of an ORCID for internal grant applications. Originality/value Although time consuming, this small-scale ORCID promotion with one department reveals that a departmental approach to ORCID education may lead to larger conversations about scholarly communications and a stronger relationship between faculty and the library.
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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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".