Scaling the INGO: What the Development and Expansion of Canadian INGOs Tells Us
Bibliographic record
Abstract
The literature on international non-governmental organizations (INGOs) has focused primarily on large INGOs, which capture the majority of total INGO spending but represent a small number of total INGOs. Over the past two decades, the number of INGOs has more than tripled throughout the global North, which has ushered in a decentralization of the sector as an emerging class of small- and medium-sized INGOs increasingly share the same space once occupied solely by large INGOs. This study focuses on these INGOs in transition to explore how they differ from large INGOs that receive significant government funding and their pathways to scale. Using an original dataset of 1371 Canadian INGOs, we explored comparative differences related to funding sources, overhead, organizational age, country coverage, staff, and religion between the transitioning and small-scale INGOs. Our results identified several general insights for how INGOs transition: (1) Large INGOs are less likely to articulate a religious motivation, which may impede government funding; (2) INGOs are more likely to be headquartered in Ontario, which is closer to federal government offices; (3) low overhead expenditures inhibit small-scale INGOs from transitioning to medium- and large-scale INGOs; (4) organizational age plays a critical factor to scale-up as INGOs increase their experience and expertise; (5) generous compensation to attract talented staff offers an under-valued pathway to scale. Finally, our results demonstrate the diversity among INGOs in Canada and problematizes singular scale-up pathways, while underscoring the necessity of future research to explore scaling strategies through individual case studies.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".