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Record W2328160727

BUILDING CAPACITY TO LIVE AND WORK TOGETHER AT AN ECOVILLAGE IN SUPPORT OF SUSTAINABLE COMMUNITY: A CASE STUDY

2014· dissertation· en· W2328160727 on OpenAlexaboutno aff
Lisa Mychajluk

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityIndividualismCollectivismSustainabilityWork (physics)Sustainable communityCommunity developmentFace (sociological concept)GlobalizationSociologySustainable livingSustainable developmentPolitical scienceEconomic growthEngineeringSocial scienceEconomicsEcology
DOInot available

Abstract

fetched live from OpenAlex

Ecovillages are important models of sustainable community and reflective of an alternative lived paradigm that values collectivism over individualism and cooperation over competition, in pursuit of bio-regionally-based, shared prosperity. In the face of growing threats to the predominant social and economic models of individualism, globalization, and unfettered growth (e.g. the decline of cheap oil), some experts have postulated that the greatest contribution that ecovillages can make is to help us understand of how to live ― smaller, slower and closer (Litfin 2013) - in other words, how to organize socially and economically in a post-carbon world. Through a qualitative case study of Whole Village ecovillage in Caledon, Ontario, this thesis explores the structures and processes through which ecovillagers build capacity for living and working together, and reveals the complex interplay between elements of community building, community dynamics and capacity building, which can either support or undermine the development of sustainable community.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.009
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.317
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2014
Admission routes1
Has abstractyes

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