Outsourcing accountability: Extractive data practice and inequities of power in humanitarian third-party monitoring
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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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 | 0.121 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.070 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".