“Preparing” and “Repairing” Public Debate: Organizational Learning of Promoters in Environmental and Energy Governance
Bibliographic record
Abstract
The deliberation experience, new imperative for public action (Blondiaux and Sintomer, 2002) produces some forms of learning that set in a new way the distribution of resources in punctual actors system that create infrastructure projects. If deliberative procedures could appear like moments for “metre à plat” values, ideas and solutions in a equilibrated, informed, respectful and transparency exchange, could we expect that it will suspend the power manifestations and the “rapports de force” in the pursuit of interests for stakeholders ? Analyzing the operation of environmental and energetic governance at the individual project level, from the promoter’s point of view, by looking at four extra-high-voltage (EHV) transmission line projects in France and Québec, and the consultation and deliberation procedures applied in each case, we argue that promoters learn better and quicker than the other stakeholders that are concerned by the large infrastructure projects. The radical imbalance of resources, experience and learning capacity among the actors tends to promote negotiation, before and after the public debate, with the actors considered relevant by the promoter, emptying the public debate of much of its content by leaving only the most antagonistic parties.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".