Recherche collaborative d’informations : repenser l’architecture des systèmes de recherche d’information à l’ère du numérique
Bibliographic record
Abstract
Cet article présente un prototype de système de recherche collaborative d’informations créé dans le cadre des travaux du laboratoire Paragraphe. Ce prototype apporte quelques éléments de réponse à des problèmes liés à la recherche d’informations à l’ère des bibliothèques numériques où, d’une part, les corpus de documents sont devenus trop gros et souvent peu structurés, et d’autre part, les utilisateurs se retrouvent isolés devant l’écran tout au long de leurs recherches. Afin de résoudre le premier problème, nous proposons l’exploitation des apports de la technologie Xtensible Markup Language (XML) pour la structuration et la recherche d’informations et pour le problème d’isolement des chercheurs, nous proposons un espace de travail collaboratif permettant aux utilisateurs de dialoguer et de s’entraider. Enfin, nous élaborons les perspectives de ce travail.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.025 | 0.110 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".