Babel revisited: a taxonomy for ordinary images indexing in a bilingual retrieval context
Bibliographic record
Abstract
With the large volume of digital images now accessible on the World Wide Web, users searching for images can be overwhelmed by many factors. Too many available images, images indexed with an incomprehensible vocabulary or one that is too specialized to be useful are but a few examples of issues leading to frustration. In addition, language barriers still prevent Web users from retrieving the images they need. This contribution presents the preliminary results of a study proposing to explore the behaviours of image searchers from four different linguistic communities. The purpose of this preliminary study is to examine queries formulated by image searchers to learn about the terminology used and evaluate how this terminology can be eventually incorporated into the development of a bilingual taxonomy for digital image indexing. Forty participants from four different linguistic communities (English, French, Chinese and Russian native speakers) were asked to write the queries they would use to retrieve ten images that were shown to them consecutively. Then they were invited to fill out a questionnaire on their behaviours as an image searcher on the Web. The results of this research allowed the acquisition of knowledge of user terminology standards and an assessment of how that terminology might be integrated in the develop- ment of a bilingual taxonomy for improved indexing of ordinary digital images. Moreover, since language barriers regularly prevent users from easily accessing information of all kinds, the bilingual taxonomy will constitute a clear benefit for image searchers who are not overly familiar with images indexed in English, which is still the dominant language of the Web.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".