Socially Not Acceptable: Lessons from a Wind Farm Project in St-Valentin, Quebec
Bibliographic record
Abstract
Social acceptability appears as a new public norm that major projects must meet in order to be authorized and realized. This article proposes to analyze the case of a wind farm project in the municipality of St-Valentin, Quebec, Canada near the border with Vermont, which was cancelled by the government due to lack of social acceptance, in order to illustrate the importance of this norm today. The project involved the construction of 25 turbines to generate 52 MW of power. Launched in 2006, the project was already significantly under way by 2008; however, in 2011, the government permanently shelved it. Through a combination of document analysis and 11 interviews, we identified the main reasons for the lack of social acceptability: lack of upstream consultation from the developer and wrong scale planned for the consultation process, controversies surrounding the public decision-making process, profound contradictions between the community’s values and interests and the nature of the project, and perceptions of the impacts on the landscape and conflicting uses. For better project social acceptability, lessons learned from this case suggest from a procedural perspective opting for broad, open, and early consultation, prioritizing a regional scale for the approach and acting with transparency, clear rules and a concern for building an ongoing relationship with stakeholders. From a more substantive perspective, our analysis points to the importance of factoring in the level of compatibility between the nature of the project and the values and interests shared by stakeholders in the community, and planning potential modifications to adapt the project to the context in light of their demands.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".