Seeing like a donor: the unintended harms of rendering civil society legible
Bibliographic record
Abstract
Following the Grand Bargain, there has been increasing focus on aid localisation and partnerships between international and local aid agencies. Yet there has been less scholarly attention on how and why international agency policies and partnerships can cause unintended harm to civil society organisations and their staff. Drawing on James Scott’s seminal work Seeing like a State, and interviews with Myanmar civil society organisation leaders in 2023, this article argues that international agencies often attempt to render civil society “legible” through processes of systematisation and codification. However, these processes can in turn sideline accrued experiential and contextual knowledge, or metis, which is necessary for local organisations’ survival, especially in times of instability. The article highlights several instances in Myanmar where the marginalisation of this more contextual knowledge results in unintended harms. The article concludes that international agencies’ acknowledgement of metis is a crucial and yet still under-recognised pillar of aid localisation.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.041 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".