Digital image description: a review of best practices in cultural institutions
Bibliographic record
Abstract
Purpose This paper aims to present the results of the first phase of a research project aiming to develop a bilingual taxonomy for the description of digital images. The objectives of this extensive exploration were to acquire knowledge from the existing standards for image description and to assess how they can be integrated in the development of the new taxonomy. Design/methodology/approach An evaluation of 150 resources for organizing and describing images was carried out. In the first phase, the authors examined the use of controlled vocabularies and prescribed metadata in 70 image collections held by four types of organizations (libraries, museums, image search engines and commercial web sites). The second phase focused on user‐generated tagging in 80 image‐sharing resources, including both free and fee‐based services. Findings The first part of the evaluation showed that each resource presented comparable information for the images or items being described. Best practices and implementation proved to be largely consistent within each of the four categories of organizations. The second part revealed two trends: in image‐upload systems, there was a virtual absence of mandated structure beyond user name and tags; and in stock photography resources, the authors encountered a hybrid of taxonomies working in combination with user tags. Originality/value The analysis of best practices for the organization of digital images used by indexing specialists and non‐specialists alike has been a crucial step, since it provides the basic guidelines and standards for the categories and formats of terms, and relationships to be included in the new bilingual taxonomy, which will be developed in the next phase of the research project.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".