La génération des connaissances au secours du management de projet ? Le cas du système d’information d’un établissement de santé
Bibliographic record
Abstract
Le management de projet qui s’est développé depuis les années 1940, ne prend pas en compte aujourd’hui la théorie de la génération des connaissances (knowledge creation theory). En adoptant une épistémologie de la pratique et à l’aide d’une recherche-action, nous montrerons que le déroulement du projet produit les connaissances nouvelles dont il a besoin (knowing) grâce à des microprocessus d’interactions. Ils mettent en jeu des connaissances tacites au sens de Polanyi repris par Duguid et Tsoukas. Le projet est alors traversé par un ensemble de tensions et de controverses au sens d’Engeström. Il en découle des implications managériales en matière de pilotage de projet.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".