Battalion leadership in the Essex Scottish Regiment and the 4th Canadian Infantry Brigade during the Second World War
Bibliographic record
Abstract
This thesis project began with the general idea of examining leadership at the battalion level in the Canadian Army during the First World War. After having been brought to my attention some time later, the Essex Scottish Regiment during the Second World War piqued my interest. This unit received the highest number of casualties of any Canadian unit throughout the Second World War, yet I had not heard or read anything significant about it. I had read gallant histories of the Royal Hamilton Light Infantry and the Black Watch, of the Regina Rifles and the Calgary Highlanders; none of these units, however, had experienced either the level of casualties or the level of historiographical poverty that the Essex had. Indeed, the Essex Scots had a head start on most other Canadian units by the beginning of the Normandy Campaign, in terms of casualties, since they had landed on the main beaches of Dieppe with their sister battalions the Royal Hamilton Light Infantry (RHLI or Rileys) and the Royal Regiment of Canada (RRC or Royals), who landed at Puys. I found myself asking what it was about the Essex Scots that made them lose so many men. Why was it that this particular regiment, that had so often been committed to battle alongside its more "successful" sister battalions, suffered more than another? What factors can account for the varied battlefield performance of the three regiments that constituted the 4th Canadian Infantry Brigade? Was it leadership, circumstance, luck, or something else? Consequently, the original idea of examining leadership at the battalion level became subsumed in this myriad of possibilities.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".