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Record W4388128750 · doi:10.1080/07352166.2023.2260511

Residential preferences, place alienation, and neighborhood satisfaction: A conjoint survey experiment in Toronto’s inner suburbs

2023· article· en· W4388128750 on OpenAlexaffabout
Daniel Silver, Prentiss A. Dantzler, Kofi Hope

Bibliographic record

VenueJournal of Urban Affairs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAlienationRecreationSociologyDisconnectionSocioeconomic statusDemographic economicsSocioeconomicsSocial psychologyGeographyPsychologyEconomicsPolitical scienceDemography

Abstract

fetched live from OpenAlex

In this article, we study neighborhood preferences among residents of highly diverse, lower income suburban neighborhoods in Toronto, Ontario. By extending the typical application of conjoint designs to the urban domain, we show techniques for measuring place alienation—a sense of disconnection from place—and its impact on neighborhood satisfaction. We find that residents in lower SES neighborhoods share many of the same priorities as residents in higher SES neighborhoods when it comes to safety, transit, school quality, neighborliness, public spaces, and building types. However, differences appear across a range of preferences including bike usage, local commercial spaces, and cultural and recreation facilities. When considering place alienation and neighborhood satisfaction, we find a consistent, robust inverted relationship—as place alienation decreases, neighborhood satisfaction increases. Moreover, this relationship is not mitigated by socioeconomic factors, neighborhood conditions, or even attitudinal and experiential factors. We end with suggestions for future research.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.314
Teacher spread0.278 · 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 designSimulation or modeling
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 routes2
Has abstractyes

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