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 – The purpose of this paper is to develop a search engine dedicated to image retrieval in a bilingual (French and English) context. This paper presents the first phase of user testing that was carried out to validate and refine SINCERITY, the new search device. Design/methodology/approach – This first phase of the search engine testing involved a small group of image searchers (10 French-speaking and 10 English-speaking participants) who were asked to retrieve a sample of images (30) using the new tool. A questionnaire was also developed to compile the comments of the users. Findings – The results of this first phase of testing revealed that even though image indexing was sometimes problematic, the participants did not encounter major difficulties retrieving images with SINCERITY. Comments and suggestions received will be taken into consideration to improve the performance and aesthetics of the search engine. Originality/value – Once fully operational, SINCERITY will allow users to search images in an attractive and user-friendly manner. Eventually, other types of images (documentary and artistic) will be added to the image database linked to the image search engine, as well as other languages.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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