Bibliographic record
Abstract
What does causation mean in conflict studies? Using a sample of published qualitative, article-length studies on the Rwandan and Yugoslav wars, we find a lack of reflexivity over causal claims in scholarship on conflict. Causal language is not as pervasive as expected, asserted cause-effect relationships are rarely fully explicated, and scholars under-explore their causal assumptions. Considering that ideas on causation necessarily condition explanations of conflict, including “ethnic” conflict, this is a major research issue. While there exists a lively debate between different causal narratives regarding the onset of conflict—with studies alternatively stressing “attitudes,” “conditions,” or both—it stops short of addressing issues at the deeper level of causal understandings. For the most part, studies subscribe to the search for empirical generalization, thus limiting attendant debates to a single model of causation. These findings indicate that conflict studies literature would benefit from greater reflexivity and pluralism with regards to causation, and paying more attention to philosophical debates on the subject. We provide a basic outline of this reflexive agenda.
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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 | 0.081 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.008 | 0.071 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".