Gestion de projet et expéditions polaires: Que pouvons-nous apprendre?
Bibliographic record
Abstract
La complexite grandissante des projets ainsi que l’incertitude inherente a certains types d’entre eux rendent souvent inefficientes les pratiques et procedures traditionnelles de gestion construites sur l’hypothese que tout est connu des le demarrage. Un nouveau regard sur la planification est necessaire afin de conserver une flexibilite tout au long du processus. Dit simplement, le projet doit emerger ! Les projets en environnement extreme, telles les expeditions polaires, peuvent etre une source d’enseignement pour les projets plus classiques au sein des entreprises dans le contexte economique d’aujourd’hui. En effet, ils presentent un fort potentiel d’apprentissage sur la gestion des situations inattendues et imprevisibles. Cet ouvrage rassemble les communications de chercheurs francais, suedois et quebecois sur le theme Gestion de projet et expeditions polaires : que pouvons-nous apprendre ? tirees d’un colloque tenu en juin 2009 a l’Universite du Quebec a Montreal.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".