Impact assessment as planning (not permitting): factors affecting its potential to influence project design
Bibliographic record
Abstract
In addition to informing regulatory decisions about developments as proposed, impact assessment (IA) can influence and help improve project design, especially when conducted at an early and flexible stage of planning. This study investigates the relationship between proponents’ design activities and IA processes and requirements, through surveys of Canadian proponents, IA practitioners and regulators. It finds that proponents often see benefits in allowing IA to inform project design and are inclined to enter the process early, but this may be impeded by the level of project detail expected and other procedural considerations. IA regulators had varying perspectives about whether IA can or should influence project design and provided views on the relative importance of project definition and firmness versus flexibility for IA purposes and on how this balance may be achieved. Suggested approaches to improve future IA practice include: 1) early and systematic consideration of environmental concerns in proponents’ initial (pre-IA) planning; 2) encouraging and enabling early design stage IA initiation; 3) early and selective identification of necessary design details, based on IA requirements and risk; and 4) eventual IA reporting should demonstrate that (and how) concurrent project design work has considered and addressed IA-identified concerns.
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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 | 0.084 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".