Que savons-nous sur les réseaux du conseil d’administration ?
Bibliographic record
Abstract
Cet article propose une revue de littérature sur les réseaux sociaux du conseil d’administration et leur influence telle qu’examinée dans les revues majeures de finance et de comptabilité. La littérature empirique dans ces champs confirme l’existence et le rôle significatif de ces réseaux, notamment lorsqu’il s’agit de choix comptables, de gouvernance et de performance des sociétés. Après avoir clarifié un certain nombre de concepts, nous proposons, à partir de travaux empiriques rigoureusement sélectionnés, d’identifier et d’analyser ce que nous savons de l’influence des réseaux du conseil d’administration. Nous pouvons ainsi suggérer des prolongements possibles pour ce thème de recherche en tirant parti de l’écart de traitement de l’effet des réseaux dans le champ CCA et en dehors du champ CCA, mais aussi des avancées méthodologiques proposées dans certains articles.
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 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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".