CANADIAN MILITARY LAW SENTENCING UNDER THE NATIONAL DEFENCE ACT: PERSPECTIVES AND MUSINGS OF A FORMER SOLDIER
Bibliographic record
Abstract
The Canadian Charter of Rights and Freedoms recognizes the existence of the military justice system and its own tribunals operating in parallel to the Canadian criminal law system. Yet, there continues to be an absolute paucity of any reference works on military law and members of the Canadian military bar are seldom heard or read. This article aims at filling the void, at least in part. In writing this article, the author, who served for 34 years in general staff and command positions in the Canadian Forces, had two general purposes in mind: a) to present the general reader with a general overview of the history, customs, organization, and structure of the military personnel system, and b) to provide a reference work presenting a detailed view of the Code of Military Discipline, both in its contents and its workings. Finally, in light of the extensive changes in 1999 to the National Defence Act and the accompanying regulations, the author concludes by reviewing the nature and impact of each of the punishments that may be imposed by a military tribunal or the Court Martial Appeal Court of Canada to those who are subject to the Code of Service Discipline.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".