Internal Instability and Technology: Do Text Messages and Social Media Increase Levels of Internal Conflict?
Bibliographic record
Abstract
Political instability and internal conflict impact the lives of thousands every single day. Understanding when and why these conflicts occur has been the focus of governments, nongovernmental organizations, and scholars for years. Predicting internal conflict was originally qualitative in nature, based on the advice and predictions of regional and country experts. More recently, as computational technology has become more widely used and effective, the efforts at predicting internal conflict have become more quantitative. This paper builds off the work of prior scholars to explore the impact of technology on internal instability. A logit model is used to test the effect of particular independent variables – specifically cell phone and internet users – on internal instability during the Arab Spring. The results show that political factors and technology are significant in explaining internal instability during the Arab Spring, while economic factors had little statistical significance or impact on the predictive probability of the model. Access to, and the use of technology will only continue to grow. As it does, it is vital that governments, NGOs, and scholars acknowledge its growing role in driving social and political movements and its impact on internal instability.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".