Meaningful Public Participation in the Proposed Federal Impact Assessment Act (IAA)
Bibliographic record
Abstract
Meaningful public participation has been a goal of environmental assessment (EA) processes for a long time. The implementation of this goal has, however been mixed, and the federal environmental assessment process is no exception. While jurisdictions have struggled to reform their EA processes over time to improve public participation, the role of the public in EA has evolved significantly. As the focus of EA has shifted from a technical exercise of predicting biophysical impacts of proposed undertakings to a consideration of a full range of social, economic, health and cultural impacts of proposals, the need for effective and meaningful public engagement has become more and more pressing. The Multi-Interest Advisory Committee (MIAC), established by the Canadian Environmental Assessment Agency for input into the federal law reform effort, developed the following principles for meaningful participation: Participation begins early in the decision process, is meaningful, and builds public confidence; Public input can influence or change the outcome/project being considered; Opportunities for public comment are open to all interested parties, are varied, flexible, include openings for face to face discussions and involve the public in the actual design of an appropriate participation program; Formal processes of engagement, such as hearings and various fora of dispute resolution, are specified and principles of natural justice and procedural fairness are considered in formal processes; Adequate and appropriate notice is provided; Ready access to the information and the decisions at hand is available and in local languages spoken, read and understood in the area; Participant assistance and capacity building is available for informed dialogue and discussion; Participation programs are learning oriented to ensure outcomes for all participants, governments, and proponents; Programs recognize the knowledge and acumen of the public; and Processes need to be fair and open in order for the public to be able to accept a decision. \nIn this post, we consider the changes to the federal assessment process reflected in Bill C-69 through the lens of meaningful public participation. We do so in three steps. We first look at what the Expert Panel on federal EA reform recommended with respect to public participation. We then consider the changes reflected in Bill C-69. Finally, we conclude with our assessment of the effectiveness of the proposed changes, and recommend adjustments to realize the goal of meaningful public participation.
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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 | 0.136 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.030 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".