CHARACTERIZING PROJECT AND STRATEGIC APPROACHES TO REGIONAL CUMULATIVE EFFECTS ASSESSMENT IN CANADA
Bibliographic record
Abstract
Advancing cumulative effects assessments (CEA) to the regional scale, spatially and strategically, has been well argued but slow to evolve. Part of the problem is that "regional" CEA is a flexible concept, varying considerably in form and function from the project to the more strategic levels. This paper steps back from current discussions of assessment frameworks and methodologies to present a typology of regional approaches to CEA based on its multiple characteristics, functions, and expectations. Drawing upon current literature and interviews with international practitioners, we conceptualize regional CEA from two broad perspectives: EIA-driven approaches and SEA-driven approaches, illustrated with Canadian case examples. Each approach to CEA has its own merits that make it suitable to address particular types of cumulative problems at different tiers of assessment, and each of which can be expected to deliver different types of assessment results. The failure to fully recognize this "one concept–multiple form" characteristic is, in part, why the EA community has struggled in developing supportive methodological and institutional frameworks for regional CEA. We demonstrate that many of the disappointments with CEA are not the result of EIA-driven applications per se, but rather the result of mismatched CEA frameworks and expectations.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".