Bibliographic record
Abstract
Pour plusieurs institutions financieres, la capacite d'implanter une gestion par projet s'avere de plus en plus source d'avantage concurrentiel. Toutefois, la singularite et le caractere temporaire du projet, l'eparpillement des connaissances entre projets, la dualite de l'autorite a laquelle les talons sont soumis, la multitude des intervenants et la pression sur les ressources posent plus d'un defi. L'ouvrage a pour objectif de presenter le «bureau de projet» comme solution organisationnelle, mise de l'avant dans plusieurs organisations, visant a tirer le meilleur des apprentissages inter et intra projets et a bâtir un capital de connaissances. Tout au long de cet ouvrage, le lecteur est invite a explorer la realite des bureaux de projet (structure, fonctionnement et meilleures pratiques) au sein de quatre institutions financieres au Canada. A l'issue de cette exploration, l'ouvrage presente une demarche en sept jalons permettant d'implanter des strategies d'apprentissage en contexte multi-projets sur differents plans: technologique, humain, structurel et organisationnel.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".