Re-grounding cumulative effects assessments in ecological resilience
Bibliographic record
Abstract
Cumulative effects assessments (CEA) evolved to holistically understand and account for the impact of a spectrum of human and natural disturbances on ecosystems. Yet, the practice of CEA has struggled to overcome siloed and reductionist underpinnings common in the impact assessment arena. One way to move CEA towards more integrated approaches is by drawing on the concept of ecological resilience. Despite gaining considerable attention in other academic spheres, however, ecological resilience remains largely unexplored in CEAs. Motivated by this gap, the objective of this article is to explore how CEAs can be reimagined through an ecological resilience lens to cultivate more integrated and holistic CEA practices. We provide a brief synthesis of CEA theory and practice, highlighting where reductionist, disciplinary, and siloed approaches prevail. Then, we explore three shifts that could recast CEA through the concept of ecological resilience: (1) a shift from valued ecological components to values/identity (resilience pivots), (2) a shift from baseline assessments to ecological trajectories, and (3) a shift from management thresholds to safe operating spaces. We argue that intersecting the practice of CEA with the concept of ecological resilience offers a real opportunity to extend beyond simply being passive respondents to an incremental “death by 1000 cuts” to cultivating the conditions needed for ecological adaption and transformation along desirable pathways.
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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 | 0.063 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.007 |
| 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".