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Record W4415586504 · doi:10.21083/crrf.v29i1.7733

HeddlestoneCohousing: A Beautiful Collision ofSustainability, Psychology,Economics, and Architecture

2025· article· W4415586504 on OpenAlexaboutno aff
Todd Kettner, Steven Kaup, Andrew Earnshaw

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsnot available
Fundersnot available
KeywordsCorporationArchitecturePlan (archaeology)Sustainable developmentPsychological resilienceCommunity developmentDevelopment planSnow

Abstract

fetched live from OpenAlex

“Cohousing is now a well-established housing option in Denmark… Since Gudmand-Hoyer began discussing his ideas for a cooperative living environment nearly three decades ago… the average size of individual residences in new communities is almost half of what it was at the original projects… the increasing willingness of residents to live close together reflects growing confidence in the cohousing concept, as people recognize its benefits and learn from existing communities.” - Danny Milman, Canadian Cohousing Network. The designer/owners of Heddlestone came together in land ownership and residential development to enjoy the mutual benefits of community living. We embrace diversity, mindful communication, sustainable design, and an environmental ethic of living lightly. We build community resilience by cultivating food, working on the land, and cooking meals together in our beautiful common house. We exchange accounting, farming, policy writing, childcare, and construction skills with each other while sharing a tractor, tools, and snow shovels – instead of snow blowers. We are multigenerational - 20 children/teens, 35 young (ish) adults, and 6 energetic elders who plan to age in place. Our community of 24 homes on 24 acres was completely sold-out even before the last foundations in our $8,000,000 development were poured. We formed our own development corporation and utilizing our collective social networks to market the homes. The resulting $1,400,000 in savings was distributed back to ourselves as homeowners ($800,000) and invested in our common house ($600,000) complete with commercial kitchen, dining area, childcare space, teen room, fireside library, and guest suites.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.018
Scholarly communication0.0080.008
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.012
GPT teacher head0.304
Teacher spread0.292 · 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 designCase report
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

Citations0
Published2025
Admission routes1
Has abstractyes

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