Policy support for rural economic development based on Holling’s ecological concept of panarchy
Bibliographic record
Abstract
Globally, rural regions are searching for innovative growth opportunities to reinvigorate their economies. This paper examines the redevelopment of rural communities through an ecological lens – based on the notion of continuous cycles of adaptive change within complex systems as first identified within Holling’s concept of panarchy. Panarchy suggests that complex systems have more than a single equilibrium point and, instead, have some inherent resiliency based on the notion of multiple stable regimes. As such, panarchy provides a conceptual model that describes the ways in which complex social and ecological systems are organized and structured both spatially and temporally. By drawing parallels between the characteristics of ecological communities and rural economic systems, a novel framework is developed to assist policy-makers reflect on a rural community’s position along its own adaptive change cycle and, then, implement appropriate inventions to improve system resiliency – which in this case is linked with economic resiliency through development and/or growth. Supported by empirical data emerging from both key informant interviews and content analysis of current rural development policy, this work also identifies leverage points where policy intervention may be most advantageous by specifying the timing of policy instruments on the cycle. Specifically, this framework describes four leverage points, three major and one minor, to help push or pull rural regions into an area of higher 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".