L'apprentissage d'une nouvelle territorialisation des grands projets routiers au ministère des transports au Québec
Bibliographic record
Abstract
The planning of large-scaled road projects is facing a crisis of public acceptability. During the last few decades, road projects have stopped being associated with progress, to become a subject of debate among western societies. Since the 1970s, the Quebec Ministry of Transportation (MTQ) has been, and is still facing major conflicts in the planning of road projects, especially in urban neighbourhoods. The impacts to urban fabric by new road networks have created strong oppositions. Therefore, these large-scale projects are more difficult to implement in a societal context where diverging stakeholder views make it difficult to attain consensus or integrated solutions. By using the concept of organizational learning, this thesis answers a double interrogation. It searches to understand how the implementation of road projects relates to stakeholders views and the environmental settings. Secondly, it seeks to vi understand how the planning practices are evolving from those experiences. The field of study is formed by four case studies of large road projects planned by the MTQ, and the inventory of organizational changes relating to planning practices. By confronting the projects characteristics with the stakeholders positioning, at each step of the procedure, we observed the effects of the public debate on the project design. Also, the analysis of the changes in the planning practices, from one project to another, enables us to identify the organizational learning abilities of the MTQ. Our results identify that the relationship between projects and the environment are much more complex that they were previously, as a new form of
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".