“He was my best subaltern#8221;: The Life and Death of Lieutenant Herrick S. Duggan, 70th Field Company, Royal Engineers
Bibliographic record
Abstract
At 0400 hours on 21 October 1915, 24 year–old First Lieutenant Herrick “Heck” Stevenson Duggan died of wounds in Béthune, France. One of the 61,000 casualties suffered by the British Army during its failed Loos offensive (25 September to 19 October 1915), Herrick differed from the vast majority of the dead and wounded because he was Canadian, not British.\nBased primarily on correspondence between Herrick Duggan and his family during the years 1913–15, this article explores Duggan’s life and experiences leading up to, and during, the Great War. In doing so it examines how the “war to end all wars” impacted one Canadian and his family, as well as exploring the nature of British society during the early years of the war. Indeed, Duggan’s letters are a valuable source for understanding the social and military aspects of the Great War. Duggan was a candid and observant writer who held little back. He was not afraid to tender criticism and concern about the Allied war effort and objectives—not to mention government figures—when he felt it was necessary to do so. Furthermore, he was often quite open with his own feelings and emotions with regard to the position in which he found himself.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".