Bibliographic record
Abstract
Abstract This article argues that the academic conflict research community has far less impact on the policy community than the importance of its work deserves. This is so for a number of reasons. First, the scholarly and policy communities communicate badly - the former rarely seeking to make their work more accessible to the latter. This is particularly true of the work of the econometricians, which few in the policy community understand. Second, the still-dominant realist academic security studies community continues to focus on interstate wars, while tending to ignore the 90% plus of armed conflicts that take place within, not between, states. Realist theories are, moreover, largely irrelevant to the task of explaining civil wars. Third, few policymakers recognize that probabilistic theories cannot be refuted by one or several counter-examples, leading them to reject important findings for the wrong reasons. Fourth, the conflict datasets used by quantitative researchers have no official standing, are often incommensurate, are unavoidably inaccurate and ignore key measures of violent conflict. Fifth, while there is some consensus with respect to findings on the causes of civil war, there are also fundamental disagreements. Little effort appears to have been made to resolve the differences. Policymakers have neither the time nor the expertise to choose between competing explanations themselves. Sixth, while there is growing consensus that the causes of civil strife are to be found in the interrelationships between development, governance and security, divisions of labour between academic disciplines and between departments in both governments and international institutions constrain both interdisciplinary and interdepartmental collaboration. The article concludes with a number of recommendations to improve the policy impact of conflict research.
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 | 0.172 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.022 | 0.064 |
| Scholarly communication | 0.063 | 0.038 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.029 | 0.025 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".