Bibliographic record
Abstract
Between 1904 and 1918, Wallace collected tales about life in the British Army and the escapades and adventures of the troops. These led him to create the eponymous character, ‘Smithy.’The second book in the series ‘Smithy Abroad’ sees the eponymous soldier and his two mates, Nobby Clark and Spud Murphy, trying to scheme, connive, and bluff their way out of anything resembling hard graft. However, their plans have a tendency to backfire...A light-hearted and sometimes zany read for fans of humorous historical fiction. Smithy seriesInitially published in the Daily Mail, the ´Smithy´ series features a bunch of short stories about the everyday life of the soldiers in the British military.Born in London, Edgar Wallace (1875 1932) was an English writer so prolific, that his publisher claimed that he was responsible for a quarter of all books sold in England. Leaving school at the age of 12, Wallace made his first steps into the literary world by selling newspapers on the corner of Fleet Street. He worked as a war correspondent after joining the army at age 21, which honed his writing abilities. This led to the creation of his first book, ‘The Four Just Men.Wallace is best remembered as the co-creator of ‘King Kong,’ which has been adapted for film 12 times (most notably directed by ‘Lord of the Rings’ director, Peter Jackson, and starring Jack Black and Naomi Watts).However, he leaves behind an extensive body of work, including stories such as ‘The Crimson Circle’ and ‘The Flying Squad.‘
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.764 | 0.501 |
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".