Signing documents: Accountability politics and racialized suspicion in Africa's development audits
Bibliographic record
Abstract
Abstract Good governance policies in international development require nongovernmental organizations (NGOs) to translate their programs into documents that render NGOs knowable and accountable to their donors. Drawing on multisited ethnographic fieldwork in Ghana and Uganda, we examine the signatures on these documents and the labor of NGO staff to obtain the signatures of aid recipients. We argue that signatures serve as mechanisms that NGO staff use to make their good governance practices traceable and to deter donor suspicion of funding misuse. Staff dedicate significant energy to imagining how signatures may or may not trigger donor suspicion. We posit that despite NGO staff's anxiety over getting signatures right—present, matching, and signed by the correct person—signatures can only ever defer donor suspicion. Such suspicion cannot be eliminated because it is deeply entrenched in racialized logics that position Global North donors as holding expertise and African NGOs as susceptible to corruption. Because NGO staff worry about donor suspicion rather than what aid recipients communicate with their (lack of) signatures, even fake signatures can circulate just as well as authentic ones. Tracing the social dynamics of collecting signatures sheds light on the racialized injustices inherent in Africa's development systems.
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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".