SINCERITY: the making of a search engine for images indexed with a bilingual taxonomy
Bibliographic record
Abstract
Purpose – This paper aims to present the third stage of a research project that aims to develop a bilingual interface for the retrieval of digital images. The requirements and implementation of the search engine are described. Image search engines attempt to give access to a range of online images available on the web. Design/methodology/approach – The strategy of using open-source software components as much as possible was chosen for the advantages of this approach: low initial cost and accessibility to evaluate and develop enhancements independently and driven by research objectives rather than financial viability. Findings – Open-source software components can be used to develop the interface. The implementation of the image search engine and its indexes uses: Apache Solr, AJAX-Solr, jsTree and jQuery. Microsoft Translator web service was integrated into the interface to provide the optional user query translation. Originality/value – The search interface is intended to be an innovative tool for image searchers who are looking for digital images. The search interface gives the image searchers the opportunity to easily access a variety of visual resources and facilitates searching for images in two different languages (English and French).
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".