Outsourcing accountability: Extractive data practice and inequities of power in humanitarian third-party monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the early 2010s, humanitarian donors have increasingly contracted private firms to monitor and evaluate humanitarian activities, accompanied by a promise of improving accountability through their data and data analytics. This article contributes to scholarship on data practices in the humanitarian sector by interrogating the implications of this new set of actors on humanitarian accountability relations. Drawing on insights from 60 interviews with humanitarian donors, implementing agencies, third-party monitors and data enumerators in Somalia, this article interrogates data narratives and data practices around third-party monitoring. We find that, while humanitarian donors are highly aware of challenges to accountability within the sector, there is a less critical view of data challenges and limitations by these external firms. This fuels donor optimism about third-party monitoring data, while obscuring the ways that third-party monitoring data practices are complicating accountability relations in practice. Resultant data practices, which are aimed at separating data from the people involved, reproduce power asymmetries around the well-being and expertise of the Global North versus Global South. This challenges accountability to donors and to crisis-affected communities, by providing a partial view of reality that is, at the same time, assumed to be reflective of crisis-affected communities’ experiences. This article contributes to critical data studies by showing how monitoring data practices intended to improve accountability relations are imbued with, and reproduce, power asymmetries that silence local actors.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it