Réseaux sémantiques et légitimé du discours organisationnel : une illustration empirique
Bibliographic record
Abstract
Cet article propose une approche de recherche qui vise à une intégration plus systématique de notre compréhension des différents types de légitimité et des liens entre légitimité et discou3rs dans les organisations. Notre approche propose de reconstruire les discours en tant que réseaux de concepts sémantiquement liés les uns aux autres. Elle permet notamment de positionner les différents discours dans un espace discursif commun selon l’usage des concepts. La stratégie proposée met en œuvre le formalisme de la modélisation en réseau combiné à l’utilisation de techniques de codage qualitatives. La démonstration empirique de cette approche présente des résultats qui confortent sa pertinence et ouvrent la voie vers une meilleure compréhension de la construction de la légitimité organisationnelle à travers le discours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".