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Record W3185055945 · doi:10.3390/land10080778

Satisfaction with Selected Indicators of the Quality of Urban Space by Polonia in the Greater Toronto Area

2021· article· en· W3185055945 on OpenAlexaboutno aff
Kamila Ziółkowska-Weiss

Bibliographic record

VenueLand · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsResidenceRecreationTourismGeographyQuality of life (healthcare)Standard of livingSocioeconomicsQuality (philosophy)PsychologyDemographySociologyPolitical science

Abstract

fetched live from OpenAlex

The main objective of this article is to determine the quality of life of Polonia living in the Greater Toronto Area (GTA), with particular emphasis on urban quality, which influences their assessment of the standard of living in this city. The presented results of the research are based on a survey questionnaire conducted with the participation of 583 respondents. The respondents evaluated, among others: accessibility to recreational tourism in the city, public transport, possibilities of finding a job, accessibility to housing and quality of the natural environment. Assessment of the selected indicators was correlated with the application of the statistical coefficient of the chi-squared test with particular sociodemographic characteristics of the examined respondents (with age, place of residence of the respondents (Toronto, suburbs) and their duration of residence in the GTA). On the basis of the formulated research hypotheses and conducted studies, it can be concluded—among others—that the satisfaction level with regard to accessibility to housing increases with age, that people living in the GTA suburbs rate accessibility to transportation lower than people living in Toronto and that people living in the GTA for more than 20 years rate accessibility to tourism, leisure and relaxation lower than people living in the GTA for a period shorter than 20 years.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.014
GPT teacher head0.281
Teacher spread0.267 · 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 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

Citations10
Published2021
Admission routes1
Has abstractyes

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