Inter-ethnic Cooperation Revisited: Why mobile phones can help prevent discrete events of violence, using the Kenyan case study
Bibliographic record
Abstract
This paper will critically explore why mobile phones have drawn so much interest from the conflict management community in Kenya, and develop a general set of factors to explain why mobile phones can have a positive effect on conflict prevention efforts generally. Focusing on theories of information asymmetry and security dilemmas, collective action problems, and the role of third party actors in conflict prevention, it aims to continue the discussion around Pierskalla and Hollenbach’s recent research on mobile phones and conflict risk. Given the successful, high profile uses of mobile phone-based violence prevention in Kenya I will identify a set of political and social factors that contribute to the success of crowdsourcing programs that use mobile phones, and explain what makes them transferable across cases for conflict prevention in other countries. The primary findings are that a population must prefer non-violence since technology is a magnifier of human intent, that the events of violence start and stop relative to specific events, the population knows to use their phones to share information about potential violence, and that there are third party actors involved in collecting and validating the crowdsourced data.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".