Author identifier analysis: Name authority control in two institutional repositories
Bibliographic record
Abstract
The aim of this poster is to analyze name authority control in two institutional repositories to determine the extent to which faculty researchers are represented in researcher identifier databases. A purposive sample of 50 faculty authors from Florida Southern College (FSC) and Ryerson University (RU) were compared against five different authority databases: Library of Congress Name Authority File (LCNAF), Scopus, Open Researcher and Contributor ID (ORCID), Virtual International Authority File (VIAF), and International Standard Name Identifier (ISNI). We first analyzed the results locally, then compared them between the two institutions. The findings show that while LCNAF and Scopus results are comparable between the two institutions, the difference in the ORCID, VIAF, and ISNI are considerable. Additionally, the results show that the majority of authors at each institution are represented in two or three external databases. This has implications for enhancing local authority data by linking to external identifier authority data to augment institutional repository metadata.
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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 | 0.031 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.044 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".