La sélection des Élites communistes chinoises : de la politique factionnelle à l'institutionnalisation du leadership
Bibliographic record
Abstract
Résumé.Cet article défend l'idée que pour être en mesure de bien saisir la sélection des élites communistes chinoises (cadres, fonctionnaires, leaders), il faille maintenant se détourner des approches utilisant la variable factionnelle comme élément explicatif pour ensuite mettre l'accent sur les règles et les conditions formelles « nécessaires » à la nomination et à la sélection du personnel. Nous pensons que l'âge et l'expérience dans les instances du Parti et de l'État prennent progressivement le dessus sur le côté informel de la politique chinoise. Enfin, ce texte se veut une mise en garde aux chercheurs concernant l'utilisation de la notion de faction dans la politique chinoise, à ne pas accepter a priori l'existence des factions et encore moins leurs présumées influences. Abstract.This article puts forward the idea that to be able to understand the selection of Chinese communist elites (cadres, officials, leaders), we must now turn away from approaches using the factional variable as an explanatory element to instead focus on rules and “necessary” formal requirements for the nomination and selection of personnel. We believe that age and experience in both the Party and State apparatus are gradually taking over the informal side of Chinese politics. Finally, this article wants to be understood as a warning to researchers regarding the use of the notion of faction in Chinese politics. Neither the existence of factions nor, much less, their supposed influence, should be accepted a priori.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".