Public hearings in impact assessment: building on basics with deliberative participation and alternative dispute resolution
Bibliographic record
Abstract
This chapter focuses on public hearings as a specific forum for public participation in impact assessment. It begins by explaining the basics - what a hearing process is, the role of hearings in IA, common hearings features and processes, and notable critiques. Discussion then shifts to examining responses to the reality that hearings often fail to meet participants’ expectations and fall short of generating public confidence in the proposed project and in the broader IA process. Specifically, the second part of this chapter discusses how “deliberative participation” and alternative dispute resolution may to some extent address long-standing concerns with hearing processes. Illustrative examples are put forward from experiences in the Canadian, Australian, New Zealand, and US contexts. Canada’s federal Impact Assessment Act is used as a basis for discussion throughout, particularly as an illustrative example of statutorily mandated public hearings.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 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".