New Technology and the Prevention of Violence and Conflict
Bibliographic record
Abstract
Amid unprecedented growth in access to information communication technologies (ICTs), particularly in the developing world, how can international actors, governments, and civil society organizations leverage ICTs and the data they generate to more effectively prevent violence and conflict? New research shows that there is huge potential for innovative technologies to inform conflict prevention efforts, particularly when technology is used to help information flow horizontally between citizens and when it is integrated into existing civil society initiatives.1 However, new technologies are not a panacea for preventing and reducing violence and conflict. In fact, failure to consider the possible knock-on effects of applying a specific technology can lead to fatal outcomes in violent settings. In addition, employing new technologies for conflict prevention can produce very different results depending on the context in which they are applied and whether or not those using the technology take that context into account. This is particularly true in light of the dramatic changes underway in the landscapes of violence and conflict on a global level. As such, instead of focusing on supply-driven technical fixes, those undertaking prevention initiatives should let the context inform what kind of technology is needed and what kind of approach will work best.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".