Bibliographic record
Abstract
This paper applies a levels of analysis approach to analyse and de-conflate the inconsistency in the historical and historiographical legacy of the Battle of Passchendaele between two levels: the operational level of war as conducted by Field Marshal Douglas Haig, General Officer Commanding (GOC) British Expeditionary Force (BEF); and at the tactical level of war as conducted by Lieutenant-General Sir Arthur Currie in command of the Canadian Corps. By applying the levels of analysis approach to delineate the strategic and operational from the tactical levels of war the paper confirms and accepts the common view that Passchendaele was a failure at the operational level of war. Haig’s operational concept of a deep advance and general breakthrough was not achieved. Fighting at the operational level of war, the BEF’s military strategic objectives were not achieved. Consequently, the historical and historiographical legacy of the campaign has been one of futility, waste, and callous indifference, which, unfortunately, has also conflated the historical and historiographical appraisals of the achievements of those formations and units of the BEF (such as the Canadian Corps) that had achieved success fighting at the tactical level of war. Through a particular focus on the performance of the Canadian Corps, this article has de-conflated this inconsistency. Henceforward, the Canadian Corps’ victory at Passchendaele deserves to be recognised as a significant tactical victory in line with the accomplishments of the Corps in previous and subsequent tactical victories such as Vimy Ridge and Hill 70 in 1917 and The Hundred Days in 1918.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".