Potential of Bibliographic Tools for Developing Organizational Taxonomies
Bibliographic record
Abstract
Bibliographic tools were used to build an organizational taxonomy in the information studies domain. A classification scheme, three thesauri of indexing terms, and two do-main taxonomies were used to collect categories related to the Information Studies Taxonomy. Classification schemes and thesauri were found helpful in creating the structure and identifying ...Des outils bibliographiques ont été utilisés pour construire une taxinomie organisationnelle dans le domaine des sciences de l’information. Un système de classification, trois thésaurus de termes d’indexation et deux taxinomies du domaine ont été utilisés pour définir les catégories liées à la taxinomie des sciences de l’information. Les systèmes de classification et les thésaurus ont été jugés utiles pour la création de la structure et la définition…
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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 | 0.033 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.086 | 0.058 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".