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Record W4249513084 · doi:10.32920/ryerson.14653182.v1

Backyard living: developing a strategy for Canadian municipalities to implement laneway housing programs through an evaluation of Western Canadian precedents

2021· preprint· en· W4249513084 on OpenAlexaffabout
Brennan Finley

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSubdivisionMetropolitan areaBusinessAffordable housingEnvironmental planningEconomic growthPublic housingPopulationSingle-family detached homeGeographyEconomicsSociology

Abstract

fetched live from OpenAlex

The high cost of living in single-family neighbourhoods in major metropolitan cities throughout Canada is making single-family housing increasingly unaffordable. Due to the decreasing amount of developable land and the increasing urban population, the development of smaller housing forms including laneway housing are growing. Currently laneway housing is used throughout Western Canada to provide single-family living but at a more attainable price. Municipalities are realizing the benefits associated with laneway housing and its ability to maintain the character of single-family neighbourhoods and diversify municipal housing stocks. Utilising a mixed method approach of interviews and policy examination, an in-depth analysis of the City of North Vancouver, the District of North Vancouver, the District of West Vancouver and the City of Vancouver laneway housing programs uses a matrix to compare the liveability, compatibility with neighbours, suitability, effectiveness and administrative processes of each policy. Outcomes demonstrate that laneway housing is viable and that major barriers such as topography, patterns of subdivision and unequitable community engagement processes can be overcome by tailoring the policies criteria, design guidelines and administrative process.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.316
GPT teacher head0.465
Teacher spread0.148 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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