Les données et leurs impacts théoriques et pratiques sur les professionnels de l’information
Bibliographic record
Abstract
Les données sont présentes dans l’environnement informationnel actuel sous différentes formes : données confidentielles commerciales ou gouvernementales, mégadonnées, données ouvertes des gouvernements, données ouvertes liées (Linked Open Data) du Web sémantique. Comment les professionnels de l’information devraient-ils se préparer pour traiter ces divers types de données ? Nous proposons que cette préparation repose sur trois éléments : une connaissance éclairée des différents types de données en jeu, une initiation aux ressources nécessaires pour traiter chaque type et une compréhension de l’impact qu’aura chacun sur la discipline des sciences de l’information et sur la pratique des professionnels de l’information.
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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 | 0.033 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".