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Record W2407594936 · doi:10.1007/s11266-016-9725-0

An Empirical Examination of Formal and Informal Volunteering in Canada

2016· article· en· W2407594936 on OpenAlexaboutno aff
Lili Wang, Laurie Mook, Femida Handy

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsSocial capitalImmigrationSociologyCultural capitalEmpirical researchSocial psychologyDemographic economicsPsychologyPolitical scienceSocial scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Using data from the 2008 General Social Survey of Canada, this study examines the factors associated with individuals’ propensity to engage in formal and informal volunteering. The results show that social networks increase the likelihood of both formal and informal volunteering, but social trust and human capital increase only the likelihood of formal volunteering and not of informal care. The findings also reveal interesting cultural influences and regional differences in the propensity to engage in formal and informal volunteering, especially between French-speaking Canadians and English-speaking Canadians, and those living in Quebec and outside of Quebec. Native-born Canadians are more likely to volunteer than their immigrant counterparts, but they are similar to immigrants in the propensity to provide informal care. Additionally, women are found to be more likely to engage in formal volunteering and informal care than men. Theoretical and practical implications of the findings are discussed.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.024
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.002
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.011
GPT teacher head0.281
Teacher spread0.270 · 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

Citations45
Published2016
Admission routes1
Has abstractyes

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