La formation à la recherche documentaire pour développer la culture informationnelle des étudiants : À quelle condition ?
Bibliographic record
Abstract
Dans le cadre d’un travail de recherche qui vise à étudier si la formation à la recherche documentaire est légitime au premier cycle universitaire, depuis 1997, nous avons interrogé des étudiants de première année qui suivent ce type d’enseignement en France. Une de nos hypothèses de travail est que les étudiants ont des représentations du système d’information. Représentations qui guident leurs stratégies de recherche et d’exploitation de l’information. Si tel est le cas, agir sur leurs représentations en leur proposant un enseignement basé sur les sciences de l’information pourrait améliorer leurs pratiques, à condition d’adopter une nouvelle approche de la « formation des usagers », basée sur une vision nouvelle de l’activité documentaire.
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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 | 0.050 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.032 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".