Support for the use of military force to prevent secession: the case of Scottish independence
Bibliographic record
Abstract
Secessions are often understood to be inherently war-prone, perhaps because individuals have been found to strongly support governments using military force to defend their country's territorial integrity. To assess the extent to which individuals actually support using military force against co-citizens, in a survey experiment we randomly assign English and Welsh respondents to a control condition listing the United Kingdom's constituent countries and overseas territories or to a treatment scenario describing a unilateral Scottish secession. Asked about the extent to which they would support the use of military force to defend the U.K.'s territorial integrity, respondents are significantly more supportive of the use of force in the control condition. Further analyses reveal men to be more hawkish than women in the control condition, while the gender gap disappears in the Scotland condition, with men's attitudes significantly mollified. Nationalist respondents, meanwhile, are relatively supportive of the use of force regardless of treatment status. Our findings thus caution that the literature's argument about the war proneness of secessions may be overly reliant on post hoc government decisions rather than ex ante individual-level attitudes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".