Une approche intrapreneuriale pour une auto-organisation « cadrée » des projets complexes
Bibliographic record
Abstract
Depuis 1990, une petite communauté de directeurs de grands projets et de chercheurs en gestion organise une capitalisation d’expériences de conduite de grands projets complexes. Elle favorise ainsi l’apprentissage du management de la complexité. En souvenir de ses conditions d’émergence, elle s’est auto-baptisée, Club de Montréal. Sans chercher à construire un modèle de pensée unique, les membres du Club de Montréal convergent vers des principes d’auto-organisation « cadrée » pour lesquels le protocole d’action conduit aux métarègles. Nous en étudions la portée dans des expériences managériales et leurs limites. Since 1990, a small community of directors of major projects and researchers in management organizes a capitalization of experiences managing large complex projects. It promotes the learning of management complexity. In memory of his conditions of emergence, it was self- baptized Club de Montréal. Without trying to build a single model of thinking, members of the Club de Montréal converge on the principles of self-organization “framed” for which the memorandum of action leads to metarules. We are looking into the scope managerial experiences and their limitations.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".