Digital image description: a review of best practices in cultural institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.012 |
| Open science | 0.002 | 0.001 |
| 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 it