Bibliographic record
Abstract
Resilience thinking has been roundly critiqued for not accounting for the political – and inherently power-laden – structures that shape decision-making. In the light of the range of critiques as well as the increasing global momentum around resilience thinking, this paper develops the concept of ‘Negotiated Resilience’. The concept highlights processes of negotiation to situate, ground and operationalise ‘resilience’. The concept puts particular accent on the procedural orientation of resilience – it is not something that ‘exists’ and that we can uniformly define, rather it is a process that requires engagement with diverse actors and interests, both in specific places and across scales. Negotiation also inevitably entails contestation and an ongoing consideration of diverse options and trade-offs. We suggest that when considering the inherent complexities of resilience, we would do better to explicitly theorise, analyse and speak to these negotiations.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".