Presence and Accuracy of Database-Driven Identifiers and Adoption of Self-Driven Author Profiles for Nursing Faculty: A Case Study
Bibliographic record
Abstract
Poster presented at the Bibliometric and Research Impact Community (BRIC) meeting held in Montreal, Quebec from June 4-June 5, 2025.Submitted Abstract:This investigation describes the self-driven profile adoption, presence of database-driven author identifiers, and the required curation efforts of common researcher profile systems associated with the current nursing faculty at a single U.S.-based public research university with very high research output (R1 Carnegie Classification). The convenience sample of 45 faculty serves to establish a baseline of public scholarship representation by an experienced bibliometric librarian newly hired to serve as a faculty liaison. There had not previously been any systematic effort to curate author-level profiles outside of ORCID iDs. In addition to ORCID profile availability and completion levels, profiles were collected from Scopus, Web of Science, Dimensions, Google Scholar, and ResearchGate. ORCID profiles were identified for 64% of faculty. ResearchGate profiles had been self-created by 38% of faculty, followed by 16% in Google Scholar. Overall, 69% of faculty had at least one of these self-driven profiles. A total of 35 faculty had associated Scopus identifiers, with 20% showing multiple identifiers and 23% identifiers having been “claimed” as determined by ORCID linking; 37 faculty had associated Web of Science identifiers, with 27% having multiple and 16% claimed; finally, 24 faculty had a Dimensions profile with 25% showing multiple identifiers. Overall, 84% of faculty had at least one database-driven profile. Curation and data cleaning of database identifiers is underway.
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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 | 0.057 | 0.239 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".