Les éléments formels de la fiche descriptive des banques d’images et leur nécessaire normalisation
Bibliographic record
Abstract
Cet article présente l’analyse de fiches descriptives de banques d’images et leur normalisation. Il présente tout d’abord quelques concepts théoriques sur les images et leur description, sur les modes de recherche et de repérage des images, sur les métadonnées internes (EXIF, IPTC et XMP) ainsi que sur la normalisation. L’analyse des éléments de la description formelle des photographies dans 30 banques d’images tient compte des éléments potentiellement utiles pour la recherche et le repérage, de la terminologie employée, de la longueur des légendes et de la fréquence d’utilisation des métadonnées internes. L’article conclut que les fiches descriptives ne sont pas normalisées (bien qu’il existe une tendance à réutiliser quelques éléments coïncidant avec les champs des métadonnées IPTC) et recommande d’employer des métadonnées internes pour normaliser la description formelle des images.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".