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Record W2094325048 · doi:10.1177/002214650804900302

Crowding in Context: An Examination of the Differential Responses of Men and Women to High-Density Living Environments

2008· article· en· W2094325048 on OpenAlexaboutno aff
Wendy C. Regoeczi

Bibliographic record

VenueJournal of Health and Social Behavior · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAggressionPsychologyContext (archaeology)CrowdingSocial environmentPoison controlMultilevel modelInjury preventionDepression (economics)Mental healthSuicide preventionDevelopmental psychologyHuman factors and ergonomicsGerontologyClinical psychologyDemographyMedicinePsychiatryEnvironmental healthSociology

Abstract

fetched live from OpenAlex

This study examines the question of gender-equivalent outcomes of mental health and social behavior in the context of crowding stress. It tests the hypothesis that gender will influence the exhibition of stress outcomes resulting from exposure to high-density living environments, with women displaying internalized responses and men responding with externalized styles. Expanding on the types of gender-appropriate disorders examined in this area of research, I selected depression, aggression, and withdrawal as gender-specific disorders based on theory and prior research. Multilevel analyses of data from a survey of Toronto residents indicate that, while the effects of household density are conditioned by gender, support for the existence of gender-equivalent outcomes is mixed. While women living in crowded homes are more likely to be depressed, men exposed to high-density living environments do not report increased aggression. However, men report higher levels of withdrawal, and some males respond with both aggression and withdrawal.

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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.049
GPT teacher head0.326
Teacher spread0.276 · 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

Citations134
Published2008
Admission routes1
Has abstractyes

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