Introduction : Les enjeux et les défis de la fonction d’évaluation en sciences de l’information et de la communication
Bibliographic record
Abstract
Les organisations sont actuellement confrontées à de nombreux enjeux informationnels et communicationnels qui ont des incidences importantes sur leurs capacités à documenter leurs processus d’affaires et leurs prises de décisions (Smallwood, 2014), à manager le personnel et conduire le changement (Benoit et al., 2019), à communiquer avec les différentes parties prenantes. De plus, les organisations font face à des exigences normatives toujours plus nombreuses et de plus en plus strictes, qu’il s’agisse de conformité et de reddition de compte, de responsabilité sociale ou environnementale, du RGPD[1], etc. Pour répondre à ces préoccupations, la mise sur pied de processus d’évaluation est souvent requise pour s’assurer de l’atteinte d’objectifs organisationnels (Moran et al., 2013, p. 414) et de la mise en place de pratiques durables répondant aux besoins évolutifs des organisations, de leurs personnels, de leurs fournisseurs et prestataires, ainsi que de leurs clientèles.
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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 | 0.054 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".