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Record W2606290847 · doi:10.1177/0899764017703707

Associational Capital and Adult Charitable Giving: A Canadian Examination

2017· article· en· W2606290847 on OpenAlexafffundabout
Belayet Hossain, Laura Lamb

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsThompson Rivers University
FundersThompson Rivers UniversityNorth Carolina State University
KeywordsSocial capitalTobit modelDonationDemographic economicsCivic engagementSurvey data collectionEmpirical examinationVariety (cybernetics)Public economicsEconomicsSocial psychologyPolitical sciencePsychologyEconomic growthActuarial scienceLaw

Abstract

fetched live from OpenAlex

This article examines the relationship between associational capital, developed through participation in social networks, and charitable giving behavior in Canada. Empirical models are specified to determine whether a relationship exists between associational capital, formed in youth and adulthood, and secular and religious donation expenditures in adulthood. Tobit regression models are estimated using data from the 2010 Canada Survey of Giving, Volunteering and Participating (CSGVP). The results suggest that the formation of associational capital in youth and adulthood is related to larger donation expenditures, although the source of associational capital and the type of recipient organization matters. It is also found that those who participate in a variety of associations are more likely to make larger donations than those who participate in fewer types of associations. The results provide further insight into charitable giving behavior and have policy implications for public and nonprofit sectors concerned with increasing charitable donations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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.

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

Citations8
Published2017
Admission routes3
Has abstractyes

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