Bibliographic record
Abstract
Against the background of anthropogenic change, rapidly rising global temperatures and extremes of crisis across multiple spheres, the real possibility of synchronous inter-systemic failure at a level involving multiple cascading system failures (Homer-Dixon et.al. 2015) demands urgent responses. From this perspective, the need for an integrated, whole-system approach to understanding and fostering radical social and ecological transformation has never been starker. The impact of rapid and irreversible biospheric changes calls for an urgent re-thinking of the role of resilience in understanding the ability of both human and non-human communities to adapt to a vastly different environment with enormous social and economic as well as biological implications. In this context, resilience thinking and resilience theory have become major tools for understanding social and ecological change across multiple disciplinary fields. This small contribution attempts to clarify the usefulness of resilience as a framework for understanding and supporting the adaptability of social and ecological systems by centering recent work by scholars in the Intersections of Sustainability transdisciplinary research network within current resilience thinking and theory.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".