Bibliographic record
Abstract
Since the 1970s, military policies in liberal democracies have been potentially affected by the gap of legitimacies syndrome, that is, the widening of the gap between high levels of political legitimacy for using force and low levels of social legitimacy for making the attendant sacrifices. Military policies are stretched between the poles of these values. While a high level of legitimation for using force encourages bellicosity, the attenuated legitimacy of sacrifice permits only low-cost military build-ups, both in economic and human terms. Deviations from this approach may recreate legitimacy problems. EU countries, as well as Canada and Australia, have managed to adopt a policy without a legitimacy gap by limiting their use of force, but, as countries that often activate their armed forces, the US and Israel have remained very much affected by this syndrome.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".