Factors Influencing Popular Support for War
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Starting with perception of the widespread discontent with the United States war in Vietnam, scholars and politicians have presumed that war and military action are not maintainable without citizen support. Accordingly, scholars have devoted extensive effort to uncover what factors influence popular support for war. Thus there is a large literature studying both major and minor wars as well as other types of military interventions. These studies of attitudes toward war fit into two categories. The first addresses the aggregate, national level of support for wars or military interventions. These studies focus on specific characteristics of wars and interventions, including the purpose for which they are fought, the types of military actions or tactics that are planned or executed, the number of casualties anticipated or actually suffered, and the anticipated or actual success or failure of the war. Many of these studies focus on popular support for actions taken by the United States, but there are also studies of opinion in Canada, Western Europe, Japan, and elsewhere. A second category of scholarship addresses the correlates of individuals’ support for wars and military interventions. Much of this research begins with the hypothesis that individuals’ ideology and partisanship are important correlates of support for war, but additional studies have investigated the importance of gender, race, and the impact of the media on citizen support. In addition to the research summarized here, readers should consult the Oxford Bibliographies in Political Science article, “Public Opinion and Foreign Policy” by Joshua D. Kertzer, especially the section on “Public Opinion and the Use of Force.”
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it