SINCERITY: the making of a search engine for images indexed with a bilingual taxonomy
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 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).
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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