Gérer les compétences spécifiques pour préserver le capital immatériel : l’illettrisme en entreprise dans la théorie de la conservation des ressources
Bibliographic record
Abstract
Les situations d’illettrisme en entreprise questionnent la capacité des managers et des services RH à conduire des dispositifs efficaces de gestion du capital immatériel. La théorie de la préservation des ressources (Gorgievsky et Hobfoll, 2008) est mobilisée pour expliquer les gains et pertes en capital immatériel tels que connaissances, compétences, esprit d’adaptation et propension à accepter le changement. Pour les auteurs, les individus qui manquent de ressources sont peu enclins à les risquer ce qui freine l’acquisition de ressources. Une partie du capital humain de l’entreprise s’appauvrit. Nos résultats expliquent les raisons d’un cercle vicieux, et révèlent les ressources à mobiliser pour rompre cette spirale d’échec.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".