Resilience Pivots: Stability and Identity in a Social-Ecological-Cultural System
Bibliographic record
Abstract
How is cultural resilience achieved in the face of significant social and ecological change?Is resilience compatible with changed structures, functions, and feedbacks as long as identity is maintained?The concept of cultural resilience has been less explored than its older siblings ecological resilience, social resilience, and social-ecological resilience.We seek to redress the balance, drawing from resilience thinking to examine how a New Zealand Māori tribal group of landowners retained strong cultural identity and connectedness to their land despite enduring significant changes in land use, economy, tenure, and governance.The landowners negotiated radical transformations in the ecology and land use of their home lands on terms that supported matters of cultural importance.The key resilience concepts of adaptation and transformation were helpful in analyzing the trajectory of change, but fell short of representing the elements of stability that supported the cultural resilience of the landowners.The concept of resilience pivots was designed to address this conceptual gap, and to offer another heuristic to resilience thinking by focusing on stability rather than change.Resilience pivots are those elements of a resilient system that remain stable despite adaptation or even transformation of other elements of that system, and in doing so support the maintenance of the system's distinctive identity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".