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Record W4254889087 · doi:10.32920/ryerson.14639034

The Influence of Religion on Philanthropy in Canada

2021· preprint· en· W4254889087 on OpenAlexaffabout
Ida E. Berger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsToronto Metropolitan UniversityVictoria Park
Fundersnot available
KeywordsMulticulturalismVoluntary sectorPopulationVoluntary associationTurnoverPublic relationsGoods and servicesPolitical scienceSociologyMarketingBusinessEconomicsEconomyManagementLaw

Abstract

fetched live from OpenAlex

Recognition of the multi-cultural nature of the Canadian population has led companies across a wide array of business domains to reach beyond their traditional bases of support to focus on hitherto untapped communities as potential markets for their goods and services. Competitive conditions within the voluntary sector have pushed non-profits along this same path. However, no systematic Canadian research reports on the attitudes, social norms, benefits sought, expectations, opportunities, experiences, or behaviors of sub-communities in the voluntary sector. This paper examines philanthropic behavior by religion using data from the Statistics Canada 2000 National Survey of Giving, Volunteering and Participating (NSGVP). The paper compares and contrasts the voluntary and philanthropic behaviors of the Canadian population across religious groups; compares and contrasts the motivations for and perceived impediments against such behaviors; and articulates and examines a model that traces the influence of religion on voluntary and philanthropic behavior in Canada’s multicultural society. Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series,TRSM, Ted Rogers School of Management Citation:

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)0.001
metaresearch head score (Gemma)0.007
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 score0.075
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0090.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.287
Teacher spread0.272 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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