Researcher Profile System Adoption and Use Across Discipline and Rank: A Case Study at the University of Manitoba
Bibliographic record
Abstract
This study analyses the adoption and use of researcher profile systems (ORCID, Scopus Author Profiles, Web of Science Researcher Profiles (formerly Publons), Google Scholar Profiles, and ResearchGate) across discipline and rank at the University of Manitoba (Winnipeg, Canada). The purpose of the study is to assess how many faculty members have registered for and use researcher profiles and whether there are any differences in use along discipline or academic rank. The adoption rate in the current study is compared with other international studies. At the University of Manitoba, there is variance in adoption between disciplines and ranks. When comparing profile systems by discipline, Google Scholar is the primary profile system for sciences and ORCID, Publons, and ResearchGate the primary profile systems for health sciences. There is variance of publication count between disciplines. Unsurprisingly, the number of publications increases as faculty are promoted. Among the studied profile systems, ORCID is not working as efficiently as it could be. Several recommendations to increase ORCID adoption are made, including mandatory public fields and suggestions for third-party integration. As part of increasing usage of profile systems, we see academic librarians as a key component of instruction and advocacy for graduate students and faculty.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".