Bibliographic record
Abstract
La complexité grandissante des projets ainsi que l’incertitude inhérente à certains types d’entre eux rendent souvent inefficientes les pratiques et procédures traditionnelles de gestion construites sur l’hypothèse que tout est connu dès le démarrage. Un nouveau regard sur la planification est nécessaire afin de conserver une flexibilité tout au long du processus. Dit simplement, le projet doit émerger ! Les projets en environnement extrême, telles les expéditions polaires, peuvent être une source d’enseignement pour les projets plus classiques au sein des entreprises dans le contexte économique d’aujourd’hui. En effet, ils présentent un fort potentiel d’apprentissage sur la gestion des situations inattendues et imprévisibles. Cet ouvrage rassemble les communications de chercheurs français, suédois et québécois sur le thème Gestion de projet et expéditions polaires : que pouvons-nous apprendre ? tirées d’un colloque tenu en juin 2009 à l’Université du Québec à Montréal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".