Pour une lecture des problèmes complexes en PME: approche conceptuelle et expérimentation
Bibliographic record
Abstract
Les problèmes complexes affectent toutes les formes d’organisation, y compris les PME, et incitent leurs dirigeants à rechercher des outils relativement simples pour faire face à la complexité et mieux asseoir leur stratégie. Dans cet article, à l’aide d’une démarche constructiviste et à la suite d’une analyse de la complexité, nous présentons un tel outil grâce auquel nous pouvons mettre en relation les variables qui caractérisent une situation et les hiérarchiser dans un modèle de représentation comportant quatre catégories, soit leur influence, leurs enjeux, leur dépendance et leur autonomie. Cette hiérarchisation permet aux décideurs de s’attaquer rapidement aux variables qui affectent le plus les autres variables ou qui conditionnent le système par l’importance de leur influence. Nous montrons, de plus, comment peut fonctionner cet outil en l’appliquant à un cas réel d’entreprise à travers un audit complexe fournissant un plan d’amélioration continue.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".