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Record W2343983641 · doi:10.14288/1.0100061

The effects on residential opportunity structures on participation patterns in voluntary organizations

2010· article· en· W2343983641 on OpenAlexaboutno aff
William John Scheu

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicKorean Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTurnoverVoluntary associationBusinessVoluntary sectorPublic relationsPolitical scienceEconomicsManagement

Abstract

fetched live from OpenAlex

The major focus of this investigation is to explore two alternative models of individual-environment articulation for explaining individual participation in voluntary organizations. The primary question posed is whether varying voluntary organizational opportunity densities of local residential areas operate to encourage organizational participation to the same degree for all residents; or whether the density of organizational opportunity elicits differential participation depending upon an individual's personal resources, or attachments to the local residential area. The development and analysis of the problem is informed by the theory and methods of "contextual analysis." The hypotheses were tested with data from 822 respondents randomly selected from eight different "social areas" in Metropolitan Vancouver. The areas were purposively chosen from a stratified typology—similar in nature to Wendall Bell's Social Area Typology. In general, the analysis suggests that the opportunity densities of a residential areas do not act independently of, but in combination with different individual characteristics to produce differences in organizational participation of urban residents. Increased organizational opportunities present in the immediate residential environment only conditionally affect an increase in organizational memberships for the better educated or the more wealthy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.739
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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 teacher head, 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

Citations0
Published2010
Admission routes1
Has abstractyes

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