Semantic Interoperability across Digital Image Collections: Evaluation of Metadata Mapping for Resource Discovery and Sharing
Bibliographic record
Abstract
The goal of this project is evaluation of the current status of semantic mapping between cataloger-defined field names and Dublin Core metadata elements across digital image collections and identification of the most frequently occurring incorrect and null mappings. A pilot study has been conducted comparing and analyzing 20 digital image metadata templates and 659 metadata item records.L’objectif de ce projet est d’évaluer l’état actuel de la mise en correspondance sémantique entre les noms de champs définis par les catalogueurs et les éléments de métadonnées du Dublin Core à travers des collections d’images numériques et d’identifier les correspondances qui sont le plus fréquemment incorrectes et sans valeur. Une étude pilote a été effectuée en comparant et analysant 20 modèles de métadonnées d’images numériques et 659 enregistrements d’éléments de métadonnées.
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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 | 0.036 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".