Shared Sacrifice? An Inquiry into the Willingness to Perform Compulsory Military Service
Bibliographic record
Abstract
The recurring debate over mandatory military service has been revived as the U.S. all-voluntary military force is stretched to its limits in the war on terrorism. With the purpose of shedding light on preferences for compulsory military service, this article presents an inquiry into the characteristics of individuals that are more willing to perform compulsory military service. Using a national data set on high school students, one of the main insights derived from this study is that the characteristics of high school students willing to perform compulsory military service agree substantially with known characteristics of military recruits. In other words, high school students favour compulsory service in the military if they already have a predisposition to enter the military voluntarily. The research shows that the person who may be more willing to perform compulsory military service has the following characteristics: Parent in the military, low socio-economic status, conservative, male, and from the Mountain, Pacific, and Southern regions of the United States. Regional variations in willingness to perform compulsory service appear, in part, to capture regional variations in religiosity.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".