Comparing Controlled Vocabularies and Tags: Research Methodologies and Research Goals
Bibliographic record
Abstract
Tags have been compared to controlled vocabulary terms and have been suggested as replacements or enhancements in indexing. This paper explores tagging and controlled vocabulary studies in the context of studies examining title and author keywords or user search terms and uses the results to analyse 236000 PubMed records tagged in CiteULike.Les étiquettes ont été comparées avec les vedettes-matières et ont été suggérés comme remplacement ou comme addition à l'indexation conventionnelle. Cet article examine la recherche sur l'étiquetage et les vedette-matières en comparaison avec des études examinant les mots-clés de titre et d'auteur ou les mots-clés des requêtes d'usager et utilisera ses résultats pour analyser 236 000 notices catalographiques de PubMed étiquetées sur CiteULike.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.118 | 0.454 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.043 | 0.058 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".