Comparing the Australian and Canadian experiences
Bibliographic record
Abstract
The Great War of 1914¬–18 is often perceived in Britain and the former Dominions as bloody, stupid, ignorant, and unsophisticated in its conduct. Bloody it undoubtedly was. Over 900,000 British and Empire troops were killed, and nearly two million were wounded. Almost no family in the United Kingdom – nor the numerous Dominions for that matter – was left untouched. The British Empire fought in Africa, the Middle East, the Mediterranean, and as far away as German New Guinea. Yet, it was in Europe, in the great crucible of the Western Front, that the largest portion of British Empire casualties fell. The experience of the Australian Imperial Force (AIF) on the Western Front from 1916 to 1918 was inherent to the British Army’s experience. Australian infantrymen who arrived from the Mediterranean in 1916 went on to serve in almost every major British campaign on the Western Front until early October 1918. In those two years, the British Army underwent a learning process that resulted in a highly effective and disciplined force by the end of the war. The cost was high. By the November 1918 Armistice, tens of thousands of Empire troops were buried or remained unaccounted for in the shattered Picardy and Flanders landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".