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Record W1958836491 · doi:10.1080/13504509.2015.1103801

Policy support for rural economic development based on Holling’s ecological concept of panarchy

2015· article· en· W1958836491 on OpenAlexafffund
Penny Slight, Michelle Adams, Kate Sherren

Bibliographic record

VenueInternational Journal of Sustainable Development & World Ecology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaNewfoundland and LabradorDalhousie University
KeywordsEcological systems theoryLeverage (statistics)ParallelsPsychological resilienceComplex adaptive systemConceptual frameworkEconomic systemRural areaEconomicsEcologyEnvironmental resource managementEconomic growthSociologyComputer sciencePolitical scienceOperations managementPsychologySocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.263
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2015
Admission routes2
Has abstractyes

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