Has the Uniform Code of Military Justice Improved the Courts-Martial System?
Bibliographic record
Abstract
J{AS the Uniform Code of Military Justice improved the courts-martial system?"The answer is a decisive "Yes."However, such a cryptic answer would lack completeness if it stopped there and failed to re-appraise the law and to survey its current administration, pointing out certain defects in both and suggesting remedies for them.Moreover, in the sense that law is a seamless web, so is the administration of military discipline, for it reaches out beyond the law and its administration to embrace other integral elements.Certainly, any useful survey of contemporary military justice requires an examination of the problem of morale and its relation to public opinion, particularly the attitudes of parents toward military service and to its essential foundation-the maintenance of discipline.Much has been said and written about the genesis of the Uniform Military Code.It seems to me well summed in the wisdom of that great patriot, James Forrestal.When questioned about the pre-code functioning of military justice, he said, "I do not believe it is as bad as it has been painted, nor as good as some of its defenders claim.Many of the criticisms have seemed to me to be without foundation, but many of them have seemed to me to be justified."I
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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".