Bibliographic record
Abstract
Are states led by women less prone to conflict than states led by men? We answer this question by examining the effect of female rule on war among European polities over the 15th-20th centuries. We utilize gender of the first born and presence of a female sibling among previous monarchs as instruments for queenly rule. We find that polities led by queens were more likely to engage in war than polities led by kings. Moreover, the tendency of queens to engage as aggressors varied by marital status. Among unmarried monarchs, queens were more likely to be attacked than kings. Among married monarchs, queens were more likely to participate as attackers than kings, and, more likely to fight alongside allies. These results are consistent with an account in which marriages strengthened queenly reigns because married queens were more likely to secure alliances and enlist their spouses to help them rule. Married kings, in contrast, were less inclined to utilize a similar division of labor. These asymmetries, which reflected prevailing gender norms, ultimately enabled queens to pursue more aggressive war policies.
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 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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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; both teacher heads agree on what is shown here.
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".