The Consequences of Contention: Understanding the Aftereffects of Political Conflict and Violence
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
What are the political and economic consequences of contention (i.e., genocide, civil war, state repression/human rights violation, terrorism, and protest)? Despite a significant amount of interest as well as quantitative research, the literature on this subject remains underdeveloped and imbalanced across topic areas. To date, investigations have been focused on particular forms of contention and specific consequences. While this research has led to some important insights, substantial limitations—as well as opportunities for future development—remain. In particular, there is a need for simultaneously investigating a wider range of consequences (beyond democracy and economic development), a wider range of contentious activity (beyond civil war, protest, and terrorism), a wider range of units of analysis (beyond the nation year), and a wider range of empirical approaches in order to handle particular difficulties confronting this type of inquiry (beyond ordinary least-squares regression). Only then will we have a better and more comprehensive understanding of what contention does and does not do politically and economically. This review takes stock of existing research and lays out an approach for looking at the problem using a more comprehensive perspective.
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 | 0.004 | 0.006 |
| 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 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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