Image retrieval behaviours: users are leading the way to a new bilingual search interface
Bibliographic record
Abstract
Purpose – This paper aims to present the results of the second stage of a research project aiming to develop a bilingual interface for the retrieval of digital images. The main objective of this phase was to investigate the roles and usefulness of search characteristics and functionalities for image retrieval in a bilingual context. Design/methodology/approach – A bilingual (English and French) questionnaire containing closed and open questions was developed and administered to two groups of participants: 20 English-speaking and 20 French-speaking respondents. The quantitative data was analysed according to statistical methods while the content of the open-ended questions was analysed and coded to identify emergent themes. Findings – This study shows that the image search process still presents difficulties and frustration from the image searchers' point-of-view. The findings established that keyword search remains the main method compared with the use of predefined categories or searching with a similar image or a drawing. They emphasised the importance of several functionalities as an integral part of the image search process and revealed the importance of being able to search for images with words extracted from more than one language. Originality/value – The main contribution of this exploratory study is to provide an understanding of how real users search for images. Combined with the exploration of best practices for image retrieval, the analysis of real image searchers' behaviours provides the foundation for the initial organisation of the search interface model we will develop in the ultimate stage 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".