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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".