MétaCan
Menu
Back to cohort
Record W4404513261 · doi:10.1016/j.wss.2024.100231

Neighborhood ‘double disadvantage’ and child development in inner city and growth areas

2024· article· en· W4404513261 on OpenAlexaff
Karen Villanueva, Gavin Turrell, Amanda Alderton, Melanie Davern, Sally Brinkman, Lise Gauvin, Sharon Goldfeld, Hannah Badland

Bibliographic record

VenueWellbeing Space and Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersNational Health and Medical Research CouncilRMIT UniversityVicHealthDepartment of Education, Australian GovernementDepartment of Social Services, Australian GovernmentBernard van Leer Foundation
KeywordsDisadvantageGeographyInner cityEconomic geographyComputer science

Abstract

fetched live from OpenAlex

We examined whether urbanicity – living in inner, middle, outer or growth areas – was associated with children's developmental vulnerability. We also explored effects of neighborhood ‘double disadvantage’, conceptualised as living in an outer or growth area with high neighborhood disadvantage, was associated with children's developmental vulnerability. There seemed to be no relationship between the level of urbanicity and child development, but unsurprisingly children living in the most disadvantaged areas were more likely to be developmentally vulnerable. When taken together, children living in inner city most disadvantaged areas had the poorest developmental outcomes. Consequently, research investigating the impact of urbanicity on child development needs to account for neighborhood disadvantage.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueWellbeing Space and SocietySame topicUrban, Neighborhood, and Segregation StudiesFrench-language works237,207