Apprentissages et actions: étude comparative de structures multipartites
Bibliographic record
Abstract
Résumé Notre article traite des apprentissages issus d'organisations visant à gérer des enjeux sociaux complexes, soit les processus multipartites de collaboration (PMC). Notre analyse comparative de deux cas de PMC dans le domaine de l'environnement corrobore la thèse selon laquelle la diversité de perspectives de leurs participants contribue à l'émergence d'apprentissages et d'innovations. Cependant, il appert que le groupe multipartite, en tant qu'organisation, est limité dans sa capacité d'implanter des idées nouvelles et de poser des actions. Par contre, les acteurs peuvent par la suite agir sur la base des connaissances acquises et des nouveaux concepts développés au cours du PMC. De plus lorsque la diver sité de perspectives est temporairement limitée par la création de sous‐groupes de travail, des actions peuvent aussi être entreprises. Abstract Our paper addresses the issue of learning occurring in organizations put in place to manage complex social issues, namely multistakeholder collaborative processes (MCP). Our comparative analysis of two environmental MCP cases supports the thesis that the diversity of the participants' perceptual framework contributes to learning and innovation. However, it appears that the multistakeholder setting, as an organization, is limited in its capacity to implement new ideas and to engage actions. Nevertheless, our analysis suggests that participants could later on take actions on the basis of the knowledge acquired and new concepts developed through the MCP. Furthermore, when the diversity of perspective is temporarily reduced through the creation of small task teams, actions can also be taken.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".