Looking Up, Down, and Sideways: Reconceiving Cumulative Effects Assessment as a Mindset
Bibliographic record
Abstract
Despite all the effort that has gone into defining researching and establishing best practices for cumulative effects assessment CEA understanding remains weak and practice wanting At one extreme of implementation CEA can be described as merely an irritant to the completion of a projectspecific environmental assessment EA At the other extreme the conceptual view is that all effects in EA should be deemed cumulative unless demonstrated otherwise Our purpose here is to consider how we might reconceive CEA as a mindset that is at the heart of absolutely every assessment of valued ecosystem component VEC to ensure that we understand the relative contributions of various stressors and can decide when cumulative effects may foreclose future activities due to impacts on VECs Conceptually we ground the CEA mindset in the context of three lenses that must all be functioning and working together for the mindset to be operative a technical lens a law and policy lens and a participatory lens Our arguments are based on a review of the CEA strategic effects assessment SEA and regional effects assessment literatures an examination and consideration of Canadian EA and SEA case practice and our combined professional experiences Through using the Bay of Fundy in Canada as a case example we establish the concept of the CEA mindset and an approach for moving forward with implementation
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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 | 0.147 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.015 | 0.156 |
| Scholarly communication | 0.032 | 0.034 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 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".