Conceptual and Lexical Compatibility in Thesauri Used to Describe and Access Moving Image Collections
Bibliographic record
Abstract
The term-to-term comparison method was used to identify various types and levels of conceptual equivalence among five controlled vocabularies used for content representation in collections of non-art moving images. It was found that conceptual overlap is high enough to justify the pursuit of research and development work on a common basic indexing and access language that could be used to name categories of persons, objects, events, and relations most frequently depicted in non art moving image collections.Nous avons utilisé la méthode de comparaison terme-à-terme pour identifier divers types de relations et niveaux d’équivalence conceptuelle entre cinq langages documentaires utilisés pour la représentation du contenu dans des collections d’images en mouvement non artistiques. Les résultats de l’exercice démontrent que la compatibilité conceptuelle est suffisamment élevée pour justifier la poursuite des travaux visant le développement d’un langage documentaire commun utilisable pour nommer les catégories de personnes, objets, événements et relations les plus souvent décrits dans les collections d’images en mouvement non artistiques.
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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| 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".