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Record W4403204829 · doi:10.1016/j.wss.2024.100217

Effect of a neighbourhood intervention on social cohesion in Hamilton, Ontario, Canada

2024· article· en· W4403204829 on OpenAlexaffabout
Marisa Young, Erika Halapy, James R. Dunn

Bibliographic record

VenueWellbeing Space and Society · 2024
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsSt. Michael's HospitalMcMaster University
Fundersnot available
KeywordsCohesion (chemistry)Neighbourhood (mathematics)SociologyPsychologyDemographic economicsGeographyMathematicsEconomicsPhysics

Abstract

fetched live from OpenAlex

Place-based interventions are a widely used approach to redressing the consequences of concentrated neighbourhood poverty and urban segregation, but relatively few studies have examined the impact of such policies on an important social resource: social cohesion. Social cohesion is pivotal in providing a sense of community, fostering positive relationships among neighbours, and promoting collective well-being. This study examined the impact of a place-based intervention at the neighbourhood-level on social cohesion in Hamilton, Ontario, Canada using a quasi-experimental study with 881 intervention participants across six targeted neighbourhoods and 173 control participants. The findings suggest social cohesion increased in some of the intervention neighbourhoods compared to a control group of Hamilton residents untouched by the intervention. If confirmed by future investigations, these results could inform a re-focusing of place-based initiatives and a shift in their evaluation beyond health and economic impacts to provide greater focus on building durable community social assets like social cohesion.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.358
Teacher spread0.341 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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