New Technology and the Prevention of Violence and Conflict
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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