Bibliographic record
Abstract
This is the fourth edition of the practitioner’s guide to criminal and disciplinary proceedings in the Armed Forces. Since the Armed Forces Act 2006 came into force the Service Justice System has matured as new practices bedded down. It now provides a fair and robust system which uniquely meets the operational needs of the Armed Forces. The system has been tested in a series of high-profile and sometimes difficult cases and, notwithstanding uninformed criticism, it has passed those tests with flying colours. The business of the Service Courts carries on day in day out in the two fixed court centres in Catterick (Yorkshire) and Bulford (Wiltshire), and trials also take place around the world wherever British forces operate. In the past few years there have been trials in the United States, Brunei, the Falkland Islands, Belize, Germany, Cyprus, and Canada, thereby demonstrating the need for a portable system. It has also had to deal with serious crimes committed during operations. These have attracted significant media scrutiny and public interest, and, unfortunately, political commentary. But these cases could not have been fairly tried by civilians. As I said when sentencing Alexander Blackman for murder:
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.572 | 0.388 |
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".