Digital image access: an exploration of the best practices of online resources
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present the results of the first phase of a research project that aims to develop a bilingual interface for the retrieval of digital images. The main objective of this extensive exploration was to identify the characteristics and functionalities of existing search interfaces and similar tools available for image retrieval. Design/methodology/approach – An examination of 159 resources that offer image retrieval was carried out. First, general search functionalities offered by content-based image retrieval systems and text-based systems are described. Second, image retrieval in a multilingual context is explored. Finally, the search functionalities provided by four types of organisations (libraries, museums, image search engines and stock photography databases) are investigated. Findings – The analysis of functionalities offered by online image resources revealed a very high degree of consistency within the types of resources examined. The resources found to be the most navigable and interesting to use were those built with standardised vocabularies combined with a clear, compact and efficient user interface. The analysis also highlights that many search engines are equipped with multiple language support features. A translation device, however, is implemented in only a few search engines. Originality/value – The examination of best practices for image retrieval and the analysis of the real users' expectations, which will be obtained in the next phase of the research project, constitute the foundation upon which the search interface model that the authors propose to develop is based. It also provides valuable suggestions and guidelines for search engine researchers, designers and developers.
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.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".