CUMULATIVE EFFECTS RESEARCH: ACHIEVEMENTS, STATUS, DIRECTIONS AND CHALLENGES IN THE CANADIAN CONTEXT
Bibliographic record
Abstract
This paper reflects on the state of cumulative effects research in Canada and future directions and challenges. The assessment and management of cumulative effects has been an enduring theme in the impact assessment literature, and scholars have consistently identified the challenges to assessing and managing cumulative effects under regulatory, project-based impact assessment. Current research on cumulative effects is focused largely on the development of frameworks and methodologies to advance cumulative effects assessment and management from individual projects to broader regional scales, and on developing the science and tools for assessing and monitoring cumulative effects. Ensuring that scholarly research continues to shape cumulative effects practice in the future requires that scholars not only attempt to improve practice under current existing regulatory processes, but also push the boundaries to ensure that decision processes also evolve so as to be accommodating of new and innovative approaches to cumulative effects at regional scales. This requires interdisciplinary approaches and sustained research funding, both of which present practical challenges to scholars, and research programmes that are developed in collaboration with industry, governments and communities.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".