Bibliographic record
Abstract
This chapter offers an overview of the evolution of resilience thinking, from a descriptive concept in ecology to a boundary concept and approach that fosters interdisciplinary research of social-ecological systems. The first section begins with a review of the evolving conceptualizations of resilience, distinguishing between those definitions that emphasize a systems understanding and the more recent definitions of resilience that emerge from its application in specific contexts. Given the breadth of definitions and their evolving nature, the chapter synthesizes some common framings that underpin resilience research and defines three key heuristics that have served to shape resilience thought, that is, the adaptive cycle, panarchy and regime shifts. From the theoretical constructs the second section moves to a discussion of the application of resilience, including adaptive environmental management and co-management, before highlighting seven principles for building resilience in social-ecological systems that may be transferable to tourism. The chapter ends with a discussion of some of the limitations and criticisms of resilience thinking that need to be considered as resilience is adopted for tourism research and practice.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".