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Record W4391305246 · doi:10.15173/a.v3i1.3197

Evaluating Tiny Houses as a Solution to the Housing Affordability and Environmental Crises

2023· article· en· W4391305246 on OpenAlexaboutno aff
Andrea Chang

Bibliographic record

VenueAletheia · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingBusinessLow income housingNatural resource economicsEnvironmental planningEconomic growthEconomicsEnvironmental science

Abstract

fetched live from OpenAlex

Tiny houses have captured the attention of North American media, with HGTV, the major cable television channel, alone showing three tiny-house related shows: Tiny House Hunters, Tiny House Builders, and Tiny House, Big Living (“HGTV Canada”). One of the first results to appear is the official “Tiny Homes in Canada” website, which claims that tiny homes sit at “the intersection of the housing crisis and the climate crisis” (Tiny Homes in Canada). Notably, this website also proclaims that the tiny home community is a “culturally idealistic response to financial desperation” (Tiny Homes in Canada) and sustainability. Much scholarly research supports the notion that the two primary drivers for tiny house living are affordability and sustainability (Evans; Shearer and Burton). In this paper, I evaluate the actual potential for tiny houses to serve as a solution to the housing affordability and environmental crises, especially in Ontario, Canada. I find that though tiny houses have the potential to be a solution for the housing affordability and environmental crises, they are not a current solution because Ontario legislation renders them somewhat inaccessible. I also explore the lifestyle choices and social conditions associated with tiny house living, as a housing option that is both idealistic (presented as a solution to the overwhelming housing affordability and environmental crises) and pragmatic (financially feasible).

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 score0.509
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.086
GPT teacher head0.289
Teacher spread0.204 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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