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Record W3024632849 · doi:10.1177/0008429820921498

Dollars and $ense: Uncovering the Socio-Economic Benefit of Religious Congregations in Canada

2020· article· en· W3024632849 on OpenAlexaffvenueabout
Mike Daly

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsAlphora Research (Canada)
Fundersnot available
KeywordsWorshipValue (mathematics)AcknowledgementMeaning (existential)Scope (computer science)SociologyReligious valuesGeographyPolitical scienceLawPsychology

Abstract

fetched live from OpenAlex

Since the earliest days of religious worship, houses of worship have stood as symbols of meaning and value. In Canada, the social, spiritual and communal value of local worshipping congregations has long been accepted. Despite this widespread qualitative acknowledgement, few studies have considered the economic impact that these congregations provide directly to their surrounding communities. Drawing on recent research in the United States, this article offers the first quantitative national estimate of the socio-economic value of these religious congregations to Canadian society. This study offers insight into the socio-economic benefit, or “Halo Effect”, that Canadian congregations and places of worship have on their surrounding communities. The article offers two estimates, ranging in economic scope from the basic impact of congregational spending, to a more generous figure resulting from the application of Social Return on Investment (SROI) models.

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)0.004
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 score0.044
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.327
Teacher spread0.280 · 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

Citations4
Published2020
Admission routes3
Has abstractyes

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