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Record W2161581392 · doi:10.1177/1468018108095633

Urbanization, Social Capital and Mental Health

2008· article· en· W2161581392 on OpenAlexaff
Kwame McKenzie

Bibliographic record

VenueGlobal Social Policy · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUrbanizationMental healthSocial capitalMental illnessGlobalizationEconomic growthDevelopment economicsMiddle Eastern Mental Health Issues & SyndromesSociologyPsychologyPolitical sciencePsychiatryEconomicsSocial science

Abstract

fetched live from OpenAlex

One of the consequences of globalization has been rapid urbanization. The urban environment has long been considered of aetiological significance for a wide range of mental illnesses from depression to psychosis but the dynamic process of rapid urbanization adds at least two other problems that are important for psychological well-being; migration, and the need to develop supportive community structures for disparate groups that may have little shared social history. In order to investigate the links between urbanization and mental illness we need concepts that help us to understand the association between community structure and health. Social capital is such a concept. This article outlines the research linking social capital to mental health. It discusses the association between the urban environment and mental illness and then considers the possible impact of globalization and urbanization on social capital and rates of mental illness.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.369
Teacher spread0.344 · 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 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

Citations85
Published2008
Admission routes1
Has abstractyes

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