Improving the practice of cumulative effects assessment in Canada
Bibliographic record
Abstract
This paper presents the findings of a critical evaluation of 12 Canadian cumulative effects assessment (CEA) documents, and offers responsive interpretation and recommendations. The evaluation focused on environmental impact assessment (EIA) documents for which CEAs have been required. A variety of types of document have been reviewed — different jurisdictions (both provincial and federal), different types of project, and different levels of EIA (comprehensive studies and major panel reviews). Findings show that: CEA is inadequately distinguished from EIA; scoping is inadequate; and cumulative effects analysis and follow-up are weak. Based on the results of the evaluation, four actions are recommended to improve the professional practice of CEA: include CEA considerations in terms of reference; use context scoping; use more follow-up studies; and link project and regional CEA.
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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 | 0.192 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".