Assessing Metadata Categories and Visual Displays for Retrieving Digital Cultural Resources
Bibliographic record
Abstract
Focus groups tested the appropriateness of a seventeen-element categorization model for uniquely identifying and retrieving digital objects from cultural repositories. Findings suggest that, while only a subset of categories ranked as important to selecting images, the type of material and a context for searching also influence the utility of a category.Des groupes de discussion ont testé l’adéquation d’un modèle de catégorisation comprenant 17 éléments visant l’identification unique et le repérage les objets numériques des entrepôts culturels. Les résultats suggèrent que, même si un sous-ensemble de catégories sont considérées comme importantes pour sélectionner des images, le type de matériel et le contexte de recherche influencent également l’utilisation d’une catégorie.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.052 | 0.108 |
| 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".