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Record W2304989344

Condos in the Suburb: What are the Drivers Behind the Decision to Move into Suburban Condominiums?

2016· dissertation· en· W2304989344 on OpenAlexaboutno aff
Cyrus Yan

Bibliographic record

VenueUWSpace (University of Waterloo) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyTransport engineeringEnvironmental planningEngineering
DOInot available

Abstract

fetched live from OpenAlex

High-rise residential condominiums are increasingly utilized by suburban municipalities in Canada as an alternative to suburban sprawl for accommodating population growth. Despite its increasing adoption, little research exists to support the effectiveness of this growth management tool, and a key indicator of its success is on whether its suburban condo dwellers exhibits a consumption pattern and lifestyle that are more urban in character. This study offers a comparative analysis on the lifestyles and motivations of suburban condominium dwellers, their respective municipalities, and condominiums clusters located in the downtowns of Toronto and Vancouver. It finds that the suburban ‘condo boom’ is fueled by a transient population characterized by tenuous socio-demographic status, childless and small households, and is currently not in a position to replace lower density forms of housing. In addition, the characteristics indicative of an urban form of living, namely that of reduced land consumption and auto-dependency, and increased levels of active transportation and public transit use, are either absent or only realized to a limited degree. The problems of how to ensure larger, relatively affordable condominium units, and how to enhance the transit-supportiveness of the suburban downtown are distilled from the findings as key issues to overcome, and five recommendations are made to address them.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.986

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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