Negotiating for Autonomy: How Humanitarian INGOs Resisted Donors During the Syrian Refugee Response
Bibliographic record
Abstract
More autonomous humanitarian international nongovernmental organizations (INGOs) have greater capacity to determine who receives aid among conflict- and crisis-affected populations than their donor-following counterparts. The latter are more likely to become instruments of states seeking geostrategic influence in places like Syria and Ukraine. Drawing on more than 120 interviews with INGO and donor agency workers, 10 months of political ethnography among INGOs working with refugees in Lebanon and Jordan after the war in Syria, and content analysis of organizational documents, this article investigates the ways that INGOs secure autonomy from donors. In a theory-building exercise, it introduces the concept of negotiation experience to explain why some INGOs develop skills and strategies that allow them to resist donor demands. It also identifies some of the tactics used by experienced negotiators to do so. The findings have implications for who controls and is accountable for humanitarian policy and practice, as well as the abilities of state donors to influence humanitarian behavior. They call into question expectations that INGOs “scramble” for funds under conditions of funding scarcity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".