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Record W4226353621 · doi:10.54113/j.sust.2022.000012

A review of tiny houses in North America: Market demand

2022· review· en· W4226353621 on OpenAlexafffund
Daiyuan Zhang, Meng Gong, Sujun Zhang, Xudong Zhu

Bibliographic record

VenueSustainable Structures · 2022
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSt. Thomas UniversityUniversity of New Brunswick
FundersQinglan Project of Jiangsu Province of ChinaNational Natural Science Foundation of ChinaPriority Academic Program Development of Jiangsu Higher Education InstitutionsNew Brunswick Innovation Foundation
KeywordsBusinessStatus quoRecreationTourismPromotion (chess)Sustainable developmentGovernment (linguistics)Economic growthEnvironmental planningGeographyPolitical scienceEconomicsMarket economy

Abstract

fetched live from OpenAlex

The history, status quo, and prospect of tiny houses in North America were reviewed. The market demand for tiny houses in North America was analyzed according to the needs in various market segments, such as shelters for low-income and homeless people, recreational housing for vacationers, and the restoration and reconstruction after disasters. This study also discussed tiny housing communities for retirees, tourism companies offering tiny housing accommodation, and government-sponsored tiny housing projects for post-disaster reconstruction. Throughout years of promotion by tiny house enthusiasts, medium advocators, and construction practitioners, more and more people have come to realize the advantages of the tiny houses made of wood, such as energy conservation, low carbon footprint, and sustainable development. In summary, the market for tiny house in North America is in a stage of rapid development.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.009
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.050
GPT teacher head0.431
Teacher spread0.381 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2022
Admission routes2
Has abstractyes

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