The Consequences of Contention: Understanding the Aftereffects of Political Conflict and Violence
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".