Designing for resilience: permaculture as a transdisciplinary methodology in applied resilience research
Bibliographic record
Abstract
In this paper I examine the relationship between resilience research and permaculture, a system for the design and creation of human habitats, organizations, and projects rooted in ethics of sustainability, well-being, and equity. I argue that applying permaculture as a tool in research design can enable research to contribute more directly, immediately, and effectively to building community resilience. I explore this argument with reference to three case studies of research projects that involve permaculture as both research topic and methodology, at multiple geographical scales. Each of these cases provides evidence that research activities contribute to community resilience, and that this can be attributed to the application of permaculture principles and methods in research design. In particular, permaculture embeds iterative processes of action learning able to enhance adaptive capacity within communities in which it is applied. This includes transdisciplinary communities that mobilize around specific research interests and communities of place and/or practice that participate in transdisciplinary research. I suggest that this may be an instance of a general situation whereby research both incorporates and enhances existing learning processes that contribute to adaptive capacity and community resilience. I tentatively propose for such collaborations the label "Mode 3" resilience research, and suggest further research be done to identify and examine further cases in both permaculture and other fields of resilience research.
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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 | 0.092 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".