Peut-on définir l’image d’entreprise au regard de la théorie du signal ?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
L’image d’entreprise est considérée comme un concept « plurivoque et flou » (Le Moënne, 2008), chaque auteur présentant sa vision du concept (Gioia et Al , 2000). Certains spécialistes la considèrent comme un tout (approche holiste), d’autres l’intégrent dans un ensemble large (approche restrictive), ou la décomposent (approche dichotomique). En considérant l’image d’entreprise comme un signal, l’auteur montre la possibilité de proposer une démarche intégrant les trois courants permettant une recherche structurée sur le concept. Il propose une nouvelle définition de l’image d’entreprise et analyse les conséquences académiques et managériales de l’image comme signal. Enfin, il propose une méthode de management du concept.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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 it