Unpacking social-ecological transformations: Conceptual, ethical and methodological insights
Bibliographic record
Abstract
Social-ecological systems contribute to environmental change and, in turn, face its corresponding shocks and disturbances. As scholarship on resilience and social-ecological transformations grows, researchers from various disciplines continue to debate how major transitions and environmental change can be anticipated, studied or guided towards just and ethical outcomes. To this end, we apply an interdisciplinary perspective to describing key aspects of social-ecological transformations scholarship around three pressing questions: (1) What features or criteria distinguish transformations from other forms of change?; (2) What are the political, ethical, and normative concerns associated with transformations?; and (3) How can we better track, measure, and evaluate such transformations? Our insights, which emerged from a workshop with early-career interdisciplinary scholars, highlight questions of justice, equity and ethics in transformations research, and suggest that more precise indicators of change, a more explicit understanding of system boundaries, and a dual focus on process and outcomes will help advance our understanding of the social-ecological implications of transformations. We hope that articulating these challenges and recommendations in an interdisciplinary framing will help further the conversation on these critical topics and provide an accessible perspective of key considerations for multidisciplinary scholarship on social-ecological transformations.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".