Re-grounding cumulative effects assessments in ecological resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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 it