Evidence, explanation, and telling histories of violence: A response to Dragojević, Braun, and Fedorowycz
Bibliographic record
Abstract
In myriad forms, violence remains a crucial and, arguably, an increasingly dominant form of political practice by a host of actors in the contemporary world. It is thus not surprising that during the past two decades research on various aspects of violence has increased significantly. Historians have long been the central chroniclers of the violent past, but others, especially social scientists, have recently moved into the spotlight with a host of compelling analyses about the origins, dynamics, and effects of violence, including those of riots, pogroms, civil war, and genocide, among others. Today, the story of violent human behavior is one that many scholars seek to tell and explain, and in a host of different ways — from research methodology and scale, to narrative style. Yet regardless of who seeks to tell histories of violence, the question of what drives people to inflict immense pain and large-scale death on others continues to remain a perplexing question in today's world, and thus is one that remains in urgent need of attention from researchers.
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 | 0.078 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.017 | 0.053 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.027 | 0.035 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".