Bibliographic record
Abstract
In August 1932, Field Marshal Lord Allenby, formerly the commander in chief of the EEF, spoke to members of the British Legion in Portmadoc, Wales. Many of the Welsh Legionaries in the small crowd had served with him in Palestine. 1 Others had likely been with the Welsh Regiment or the Royal Welch Fusiliers in Macedonia and Mesopotamia. Fourteen years after the war had ended, he informed the gathering, he still heard and was often pulled into ‘disputes as to which theatre of operations, which front or field of war, was the scene of worst hardship. France, Palestine, Salonika, Dardanelles, Mesopotamia, or elsewhere.’ On the day, Allenby was in no mood to put one campaign above the others. All had suffered equally. ‘From what I saw of war; in Flanders, France, Palestine, and Syria’, he told the Legionaries, ‘and from what I know, from others, on other fields; I am assured that, whether in East or West, or Sea or Land; from the ice and snow of Northern Russia, to the torrid heat of East and Central Africa there was nothing to choose’. 2 His speech, in any case, was meant to impress upon the crowd of ex-servicemen the folly of war and the need to learn from past mistakes at a time when the world political situation was deteriorating and disillusionment with the war, focused overwhelmingly on the horrors of the trenches of France and Flanders, was perhaps at its height.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.234 | 0.073 |
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".