Problèmes de cohérence théorique chez Philippe d'Iribarne: une voie de sortie
Bibliographic record
Abstract
Over the last 15 years, Philippe d'Iribame has undertaken vast research on the connections between national cultures and management. He reports on corporate dynamics, particularly concrete management practices, by drawing from a rich ethnographic corpus and a historical interpretation of national cultures. In this text, we examine the coherence, foundations, and limitations of the theoretical apparatus that he has constructed to explain the relation between national culture and management. We see that while his empirical material is rich and interesting, allowing him to shed new light on cultures, his theoretical construct displays numerous incoherencies and contradictions (for example, using different concepts to describe the same empirical reality). This prevents him from constructing a coherent empirical corpus that would allow certain generalizations about culture. At the end of the paper, we propose a way out of the theoretical impasse in which d'Iribarne finds himself, by proposing a theoretical model that explains national cultures in regard to value dynamics. [PUBLICATION ABSTRACT]
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".