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Record W7079603448 · doi:10.26108/h6gc-0x89

On a wing and a prayer: How religion affects volunteering and charitable giving in Canada

2009· article· en· W7079603448 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2009
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsGenerosityReligiosityPrayerReligious identityIslamReligious organizationWorshipAltruism (biology)

Abstract

fetched live from OpenAlex

The Western world as we know it is riding on a wing and a prayer as talk of a recession looms. Canada‟s financial state is troubling and often in situations such as this volunteering and charitable donations are relied on in an attempt to perpetuate normality in a sinking world. With generosity or philanthropy being such an important part of Canada, it is necessary to examine where these resources are coming from and why certain individuals are more likely to give communal support. Religious groups and religious people have been identified as a primary group of volunteers and financial supporters to charities. This paper will explore whether or not religious people demonstrate higher levels of giving and volunteering than the general public in Canada today and why. Previous research has concluded that religious individuals are more philanthropic than non-religious people, but there is some controversy as to how this difference may be explained. Social ties and religiosity are the two primary reasons given in the literature as to why religious people are more philanthropic than non-religious people. This study finds that religious people do give more charitably than non-religious people, but that there is no significant difference between the volunteer rates of these two groups. It also finds that of the charitable donations made by religious people a large portion of them go to religious groups. The factor that was found to help explain why religious people give more was religious teachings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.973

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 studies0.0000.000
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 head0.180
Teacher spread0.174 · 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
Published2009
Admission routes1
Has abstractyes

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