Information Technology and Organizational Transformation : Solving the Management Puzzle , par Suzanne Rivard, Benoit A. Aubert, Michel Patry, Guy Paré et Heather A. Smith, Elsevier, 2004, 32 pages. Recension faite par François Bergeron, professeur à l’Université Laval
Bibliographic record
Abstract
RésuméÀ l’heure de la mondialisation des marchés et de la redéfinition du rôle de l’État, les partenariats public-privé présentent une alternative qui mérite d’être considérée. Ils permettent aux gouvernements de se désengager des opérations tout en demeurant grands architectes et arbitres. Ils leur permettent également d’améliorer la qualité et l’accessibilité des services à court et moyen terme sans alourdir le fardeau fiscal des contribuables et en contrôlant souvent plus efficacement les budgets consacrés aux services publics. Nous proposons dans cet article de répondre aux trois questions suivantes : quelle est la nature véritable des partenariats public-privé et quels enjeux soulèvent-ils? Quels sont les différents types de partenariats et en quoi se distinguent-ils de la sous-traitance? Enfin, quels sont les principaux facteurs de succès dans la conception, la mise en œuvre et la gestion des partenariats public-privé?
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".