Voluntarily Enlisting for the War
Bibliographic record
Abstract
My mother and father lived with their two years old son, Kenneth, in Edmonton, North London, and the year was 1940. Dad was 35 years old and had his own heavy goods lorry, with which he did long-distance haulage for various large and small companies. He was, therefore, in a reserved occupation and would not be subject to conscription. Nevertheless, he wanted 'to do his bit for King and Country'.As he was a first class mechanic, Dad wanted to join the RAF as ground crew. So, he set off on his bicycle against a strong headwind and, not being a keen cyclist, he soon became tired and fed up. He therefore decided to stop at the first recruiting post he came to. Hence, my father enlisted in the London Irish Regiment.When he arrived home with this news, my mother was not at all happy with her husband, referring to him in somewhat derogatory terms, especially regarding his mental stability.Having joined the London Irish Regiment, Dad was soon in trouble when, having been noted as a proficient driver and mechanic, he was ordered to be chauffeur to the regiment's senior officers. He promptly refused to obey, on the grounds that he had volunteered to serve his country in fighting its enemies, and not playing wet-nurse to a bunch of drunken officers on their regular binges. He was, unsurprisingly, immediately put in detention, commonly referred to as the 'glasshouse'. Dad responded by getting letters written by Mum, who was a competent and experienced secretary, to all the authorities that they and others could think of, including his local Member of Parliament. It was not long before Dad was released from detention, but was transferred from the London Irish Regiment into the Kings Royal Rifles Brigade, the world's fastest moving regiment. The standard marching rate is 120 paces per minute, whereas the KRRs march at 160 paces per minute, which is basically a trot. To my Dad it seemed that, while he had won one battle, the Army had definitely had the last laugh over a man approaching 36 years of age.Dad served the whole of the war in the Middle East.He told how he was often involved in driving around in the desert, before the first battle of El Alamein, in trucks camouflaged with painted canvas on timber framework made up to look like tanks and heavy artillery.Telegraph poles were used to represent gun barrels. This deception fooled the enemy into believing that the allied forces were much stronger than they really were. History records, quite surprisingly, that it really worked.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.024 |
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".