Technologizing Humanitarian Space: Darfur Advocacy and the Rape-Stove Panacea
Bibliographic record
Abstract
We examine how an unassuming domestic technology—the fuel-efficient stove—came to be construed as an effective tool for reducing sexual violence globally. Highlighting the process of problematization, the linking of problems with actionable solutions, we show how US-based humanitarian advocacy organizations drew upon spatial, gender, perpetrator, racial, and interventionist representations to advance the notion that “stoves reduce rape” in Darfur. Though their effectiveness in Darfur remains questionable, efficient stoves were consequently adopted as a universal technical panacea for sexual violence in any conflict or refugee camp context. By examining the emergence and global diffusion of the rape-stove problematization, our study documents an important example of the technologizing of humanitarian space. We postulate fuel-efficient stoves to be a technology of Othering able to simplify, combine, decontextualize, and transform problematizations from their originating contexts elsewhere. When humanitarian advocates construe immensely complex crises as “manageable problems,” the promotion of simple technical panaceas may inadvertently increase the burden of poverty for user-beneficiaries and silence the voices of those they claim to champion and serve.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".