“Pusser grub? My God but it was awful!” Feeding the Fleet During the Second World War
Bibliographic record
Abstract
When Canada declared war on Germany in September 1939 the Royal Canadian Navy (RCN), Royal Canadian Naval Volunteer Reserve (RCNVR), and Royal Canadian Naval Reserve (RCNR) consisted of perhaps 3,000 officers and men. The RCN was manning six destroyers and seven smaller craft out of Halifax and Esquimalt. While the men of the RCNR had seagoing experience through the merchant navy and the fishing fleets, only a limited number of men from the RCNVR had managed to spend any time in RCN vessels. No reservist from either category that had any significant prewar training or experience in food supply or preparation for large groups could be located for an interview. However, former navy cooks who joined just before and during the course of the war have been interviewed by this author or by other researchers, as have seamen who served with these men and consumed the meals they prepared at sea. This study will examine the validity of the statement quoted in the title. It will look at the victualling and cook trades, the drafts (postings) these men had between 1939 and 1945, the type of trade training they received, the foods they were permitted to order and were given to prepare, the conditions under which they worked in different classes of ships, how the seamen responded to their meals, and the role they played in feeding the men as well as keeping up morale and playing their part in fighting the ship.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".