Mediation in Environmental Assessments in Canada:\nUnfulfilled Promise?
Bibliographic record
Abstract
The federal environmental assessment (EA) process and most. provincial EA processes in Canada either specifically provide for mediation as an option or implicitly allow for it. Inspite of this, the actual use of mediation and other forms of alternative dispute resolution (ADR) has been almost non-existent in Canadian EA. There is an emerging view, however that mediation could be applied usefully at points of the process when there is conflict among the parties. Such adjustments in process would signal the need for approval agencies -andproponents to give serious consideration to more collaborative techniques of participation. The objective of this article is to consider how mediation has been used to date, and whether it has a role to play in improving the effectiveness, efficiency and fairness of EA processes in Canada. This is accomplished through consideration of the use of mediation in recent years and the results of interviews with twenty EA practitioners. Findings show that mediation has been mainly used in the EA context in the province of Quebec. However, most respondents felt that there is potential for the use of m'ediation to strengthen EA. Based on our findings we conclude by outlining three potential ways mediation could be used in EA: as a tool within a traditional EA process to mediate contentious issues; as a process replacement for a procedural requirement; and as a way to find an interim solution to a policygap identified in a project EA.
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 | 0.033 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.021 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".