Gestion de projet et co-construction : utopie ou voie du succès? Une réflexion exploratoire
Bibliographic record
Abstract
Depuis quelques années, plusieurs initiatives se réclament d’une vision renouvelée des parties prenantes dans les projets, passant d’une gestion « des » parties prenantes à une gestion « pour » les parties prenantes, notamment par la notion de co-construction. Que veut dire co-construire en contexte de gestion et de gestion de projet? Est-il possible d’envisager la co-construction d’un projet industriel ou commercial? Aucune recherche ne semble rendre compte d’une expérience de mise en oeuvre de la co-construction comme approche de gestion d’un projet. Cet article propose donc une réflexion exploratoire alimentée par une étude de cas en cours, en documentant les enjeux associés à la structure et au processus décisionnel de projets co-construits. L’implication des parties prenantes et le pouvoir décisionnel qui leur est accordé représentent un changement de paradigme. Malgré les défis que cela suppose, il semble tout de même possible d’envisager de co-construire un projet industriel et commercial.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".