“Going back to what really held us together”: re-adaptation as resilience in the Torres Strait Islands, Australia
Bibliographic record
Abstract
In the Torres Strait Islands (TSI), Indigenous Australian communities are negotiating the challenge of maintaining their identities and cultures in the face of rapid change. These identities and cultures are seen as vital to the region’s resilience, and yet to be resilient may mean making difficult choices about change, specifying which aspects need changing, under what conditions, and by and for whom. TSI communities have a long history of conceptualizing relationships with change that have enabled them to build resilience to navigate these. As such local, indigenous-led conceptualizations of resilience are needed as alternatives to generic, externally defined ones, and participatory co-research processes can be key to surfacing and probing these. We consider “re-adaptation” as an articulation of resilience that emerged through such a process that we undertook in the TSI to explore and build community and regional stakeholders’ capacities to deal with diverse drivers of change. Re-adaptation was proposed in this process to describe how communities might turn to past cultural practices and knowledge to address contemporary and possible future challenges. The concept suggests connections to resilience theory through three inter-related features: first, it entails a weaving of old and new, or past and future; second, it suggests a dynamic view of resilience, and pathways to achieve it; third, it represents an Indigenous, place-based relationship with change. Existing research on the importance of Indigenous knowledge in decision making for resilience supports this, and recent developments in climate policy and Indigenous rights in the TSI make it timely to give more consideration to meanings of re-adaptation. Re-adaptation reflects the “scaling deep” mode of impact, by enriching the discursive landscape through more pluralistic conversations about resilience.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.037 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| 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".