Le modèle coopératif, un atout à valoriser dans l’identité de marque employeur des coopératives financières ?
Bibliographic record
Abstract
S’appuyant sur le modèle Attraction/Sélection/Attrition (Schneider et al., 1995), cette recherche vise à explorer l’intérêt pour les coopératives financières de développer une marque employeur et d’identifier les bénéfices qui la composent (Ambler et Barrow, 1996) afin de mieux se distinguer des banques à capital-actions sur le marché de l’emploi. Des entrevues menées auprès de 21 responsables RH du Crédit Agricole (France) et de Desjardins (Québec) confirment qu’ils perçoivent que leur marque employeur se distingue de celle des banques actionnariales par des atouts symboliques, fonctionnels et économiques qui gagnent toutefois à être plus formalisés dans une identité de marque employeur claire et communiquée à l’interne comme à l’externe.
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.
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 | 0.000 | 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 | 0.001 | 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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".