Bibliographic record
Abstract
Abstract If mutinies are significant threats to those military parties facing defeat during wars, they are still more dangerous to the victors after the war is ended, when those conscripted for the duration of the war are desperate to return home. This chapter covers three such mutinies: those affecting British forces in 1918 and 1919; those facing Canadian forces in 1919; and finally the mutiny that literally grounded the RAF in 1946 in India and the Far East. The first cases occur in the south of England and France as the First World War is ending, but Churchill in particular was keen to retain both naval and army units to continue the fight against the fledgling Bolshevik regime. What is intriguing about these is just how militant the mutineers were and how the British government treated them with kid gloves, unlike those in the British Foreign Labour units who we meet in chapter 6. For the Canadian army the problem starts in Russia but end up in Wales, as the troops kick their heels waiting to return home and frustrations boil over into gunfights near Rhyl in 1919. Finally, we consider the similar issues prevailing over the RAF in India and the Far East as it becomes clear to the subordinates that they are a long way from home and have little immediate prospect of going home—unless they mutiny.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".