Sense-making : un modèle de construction de la réalité et d’appréhension de l’information par les individus et les groupes
Bibliographic record
Abstract
Le modèle sense-making offre une perspective constructiviste pour l’étude de la relation des individus et des groupes à l’information. La compréhension qu’ont ces derniers d’une situation, de son contexte et de leur résolution s’appuie notamment sur leurs connaissances, leurs expériences et leurs valeurs. Celles-ci exercent aussi une influence sur la manière dont ils reconnaissent ou ignorent l’apport d’information, puis analysent celle-ci et l’intègrent à leurs cartes cognitives ; ce faisant, ils délimitent (« énactent ») leur propre réalité dont les frontières circonscrivent leurs décisions et actions. Cet article examine l’approche de sense-making de Brenda Dervin en sciences de l’information et celle de Karl Weick en sciences de la gestion. Il expose les caractéristiques des modèles proposés, de même que les principales implications de la construction du sens chez les individus et les groupes relativement à la recherche et l’utilisation d’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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".