Comparing the Prevalence and Organizational Distinctiveness of Faith-Based and Secular Development NGOs in Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".