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Record W2908970452 · doi:10.1007/s11266-018-00072-6

Comparing the Prevalence and Organizational Distinctiveness of Faith-Based and Secular Development NGOs in Canada

2019· article· en· W2908970452 on OpenAlexaboutno aff
John‐Michael Davis

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsOptimal distinctiveness theoryFaithFaith-Based OrganizationsGeneralizability theoryLimitingOverhead (engineering)BusinessPublic economicsPublic relationsEconomic growthPublic administrationPolitical scienceEconomicsDevelopment economicsSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Abstract Faith-based development organizations (FBOs) have been argued to deliver more cost-efficient development projects than their secular counterparts through exclusive access to faith networks, which provide predictable decentralized funding, the recruitment of volunteers, low employee salaries, and less overhead and indirect costs. To date, however, comparative analyses of religious and secular organizations have relied on a case-by-case approach, limiting the generalizability of findings. This study addresses this methodological gap by analyzing Registered Charity Information Return filings and organizational websites of 844 Canadian development NGOs to determine the proportion of FBOs and their organizational distinctiveness. The results show that FBOs comprise 40% of the Canadian NGO sector in terms of the number of organizations and their expenditures in developing countries, and are significantly less reliant on federal funding ( p < .1), pay employees lower salaries ( p < .01), but do not exhibit a significant difference in their expenditures on overhead and indirect costs. Thus, Canadian FBOs participation in faith networks shapes their organizational modus operandi but does not result in a low overhead alternative to secular NGOs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.853

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 head0.007
GPT teacher head0.234
Teacher spread0.226 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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