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Record W3014486347 · doi:10.5539/jsd.v13n2p20

Moving Beyond Sustainability: A Regenerative Community Development Framework for Co-creating Thriving Living Systems and Its Application

2020· article· en· W3014486347 on OpenAlexvenueno aff
Leah V. Gibbons

Bibliographic record

VenueJournal of Sustainable Development · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
FundersArizona State University
KeywordsThrivingSustainabilitySustainable developmentScale (ratio)SociologyEngineering ethicsEnvironmental ethicsEnvironmental resource managementEnvironmental planningEcologyBusinessSocial scienceEnvironmental scienceGeographyEngineeringBiology

Abstract

fetched live from OpenAlex

Sustainable development and design have the potential to shift their aim from improving human well-being within environmental limits to catalyzing thriving social-ecological communities (i.e., living systems) across scales. Regenerative development (RD), a methodology that harnesses the potential of living systems, offers a way forward. RD integrates science and practice with essential but often neglected components of sustainability—ecological, social, cultural, spiritual, and geophysical—as well as their temporal and spatial dynamics. It also addresses the root causes of (un)sustainability—thinking and worldviews. This research creates and pilots the first community-scale RD framework (RCD Framework) in a developing intentional community. Findings indicate that the RCD Framework achieves its intended aim of facilitating shifts in thinking and development and design concepts toward holistic and regenerative. Factors that are conducive to or impede RCD are identified, and suggestions are made for advancing RCD science and practice. Implications for larger communities, cities, regions, and sustainable development and design are discussed.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.271
Teacher spread0.256 · 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 designQualitative
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

Citations28
Published2020
Admission routes1
Has abstractyes

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