MEANINGFUL PUBLIC PARTICIPATION IN ENVIRONMENTAL ASSESSMENT: PERSPECTIVES FROM CANADIAN PARTICIPANTS, PROPONENTS, AND GOVERNMENT
Bibliographic record
Abstract
The benefits of public participation in EA have been clearly described in both theoretical and practical terms. Despite the wealth of benefits ascribed to engaging the public in EA, the design and implementation of specific public participation programs remains contentious. In fact, public participation processes are still frequently criticized as dissatisfying by participants, costly, and time consuming, by proponents, and inefficient by governments. The purpose of this research was, therefore, to establish whether there are points of convergence in opinion among participant, proponent, and government sectors regarding what constitutes appropriate principles and procedures for meaningful public participation. Our approach was qualitative and included consideration of the literature and interviews with Canadian public participation practitioners. Government, proponent and participant respondents identified and agreed on eight key elements, along with sub-components, of meaningful public participation. These elements, as well as those where convergence was not reached, are outlined and discussed.
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 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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.048 | 0.026 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".