Tracing the Geography of NGOs: Exploring where Canadian Development Organizations Work and Why
Bibliographic record
Abstract
Abstract As NGOs have emerged as arguably the most prominent actors within the global development enterprise, their international activities and presence have grown to represent a key area of inquiry for development scholars. Existing literature on the geographic distribution of development NGOs leans heavily on quantitative analysis, which lends little insight into the deeper motivations behind the location-based decisions that these organizations make; this study uses a qualitative lens to fill this gap, shedding light on the question “ Why do NGOs work where they do”? After interviewing representatives from 22 Canadian development NGOs, the research team determined several key catalysts, which shape the geography of these entities. These factors include existing relationships, personal visits, local requests, logistical ease, funder restrictions, documented need, and humanitarian crises. Furthermore, the decision-making framework related to project locations appears to evolve as organizations grow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".