Le rôle du contexte dans le jugement de pertinence en situation de repérage d’information
Bibliographic record
Abstract
Cet article propose de cerner le concept de contexte en recherche d’information et de mieux comprendre son impact sur la définition de la pertinence et de son mode d’évaluation principal par l’usager, à savoir le jugement de pertinence. Après avoir abordé le concept de contexte, nous explorerons les principales définitions du concept de pertinence ainsi que quelques modèles théoriques qui démontrent l’influence grandissante de l’approche sociotechnique sur ces modèles, soit l’intégration des composantes humaines et techniques ainsi que leurs interactions. Par la suite, nous présenterons certains critères utilisés pour juger de la pertinence des résultats de recherche et nous verrons comment ces critères peuvent être affectés par différentes dimensions du contexte. Enfin, nous soulignerons le caractère dynamique et multidimensionnel des concepts de pertinence et de contexte.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".