Les hauts revenus des chefs d’entreprise sont-ils justifiés ?
Bibliographic record
Abstract
Dans le présent article, nous nous penchons sur trois types de justifications des hauts revenus des chefs d’entreprise et sur les inégalités qui en résultent. Selon la première justification, ces revenus sont justifiés par le mérite des dirigeants ; selon la deuxième, par la difficulté à remplacer les individus qui occupent ces postes ; selon la troisième, par la motivation à la performance que génèrent ces revenus. Nous tenterons de montrer qu’aucun de ces trois arguments ne permet de justifier les revenus actuels. Ils se butent à l’évaluation empirique, sont souvent indéterminés en ce qui a trait aux résultats qu’ils permettent de justifier ou sont moralement arbitraires. En fait, seule une version amendée de la troisième justification – fondée sur la motivation – passe tous les tests. L’effet de la rémunération sur la motivation permet de justifier certaines inégalités salariales, mais certainement pas de l’ampleur de celles que nous connaissons aujourd’hui.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".