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Health hazards and socio‐economic status: a neighbourhood cohort approach, Vancouver, 1976–2001

2006· article· en· W2146105966 on OpenAlexafffundvenueabout
Michael Buzzelli, Jason Su, Nhu Da Le, Tenny Bache

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Medical Services FoundationSocial Sciences and Humanities Research Council of CanadaCanadian Institute for Advanced Research
KeywordsNeighbourhood (mathematics)Metropolitan areaGeographyCensusCohortSocioeconomic statusPopulationHealth geographyEnvironmental healthSocioeconomicsRegional sciencePublic healthSociologyMedicineHealth education

Abstract

fetched live from OpenAlex

This paper lays the foundation for a research program concerned with the geographical patterning of environmental and population health at the urban neighbourhood scale. Based on the Vancouver metropolitan region, the aim is to better understand the role of neighbourhoods as epidemiological spaces where environmental and social characteristics combine as health processes and outcomes at the community and individual levels. With respect to procedure, this paper builds a cohort of commensurate neighbourhoods across all six census periods from 1976 to 2001, assembles neighbourhood air pollution (total particles) data, and provides an initial analysis to demonstrate how air pollution systematically and consistently maps onto neighbourhood socio‐economic markers, specifically education and family status. We conclude with a discussion of how the neighbourhood cohort can be further developed to address emergent priorities in the population and environmental health literatures, namely the need for temporally matched data, a life‐course approach and analyses that control for spatial scale effects.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations13
Published2006
Admission routes4
Has abstractyes

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