Learning, Public Involvement and Environmental Assessment: A Canadian Case Study
Bibliographic record
Abstract
Policy makers and scholars have shown increased interest in the learning outcomes of resource and environmental management initiatives. This applies to environmental assessment (EA) as well as to processes that more explicitly incorporate learning-related objectives, such as adaptive management. Using a transformative framework and a qualitative methodology, in this paper we investigate learning outcomes from involvement in an EA of a major hog processing facility in Brandon, Canada. We also examine implications for EA process design, and the pursuit of key social objectives of sustainability. The extent to which the EA in this case facilitated emancipatory learning was quite limited, that is, the process deviated substantially from the ideal conditions of learning. As well, the EA was at best legitimating, and was by no means participatory, empowering, or equitable. The emancipatory potential of involvement in EA, and opportunities for mutual learning, could be increased with earlier involvement, higher degrees of participation, and more open decision-making.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".