Image retrieval behaviours: users are leading the way to a new bilingual search interface
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 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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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