Pressed to Prolong: Conscription, the Costs of Military Labor, and Civil War Duration
Bibliographic record
Abstract
Abstract Existing research has identified numerous explanations for why some civil wars last longer than others. Yet, the type of labor that state militaries recruit has remained unexplored in this context. We consider how a state's military personnel system affects its ex post decision to keep fighting. We argue that conscription renders access to military labor relatively easy and, thus, less expensive. As military wages fall, war becomes less costly, the production of military power becomes more labor intensive, and the hazard of conflict termination declines. In a volunteer force, in contrast, military labor is relatively scarce and, therefore, more expensive. Accordingly, war becomes more costly, the production of military power becomes more capital intensive, and the hazard of conflict termination rises. These effects are reinforced as a conflict persists, leading to an increased divergence in duration across conscripted and volunteer militaries. We test these contentions using a global sample of civil wars, finding robust support for each expectation. We also validate the underlying mechanisms linking conscription to protracted conflict in two illustrative cases. Our results highlight the importance of labor-side determinants of war duration and contribute to a growing literature that explores how the composition of military forces affects conflict dynamics.
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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".