Crowdseeding in Eastern Congo: Using Cell Phones to Collect Conflict Events Data in Real Time
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
Poor-quality data about conflict events can hinder humanitarian responses and bias academic research. There is increasing recognition of the role that new information technologies can play in producing more reliable data faster. We piloted a novel data-gathering system in the Democratic Republic of Congo in which villagers in a set of randomly selected communities report on events in real time via short message service. We first describe the data and assess its reliability. We then examine the usefulness of such "crowdseeded" data in two ways. First, we implement a downstream experiment on aid and conflict and find evidence that aid can lead to fewer conflict events. Second, we examine conflict diffusion in Eastern Congo and find evidence that key dynamics operate at very micro levels. Both applications highlight the benefit of collecting conflict data via cell phones in real time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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