Bibliographic record
Abstract
There is a curious paradox about the Canadian Corps that is summed up in this quotation from Canadian Brass, Stephen J. Harris’s study of the evolution of a professional army in Canada. How did this military organisation become so effective in war, considering the background it had and the structure that supported it for most of its existence? This model of tactical excellence was born amid the chaos of Canadian Minister of Defence Sam Hughes’ egomaniacal control at Valcartier Camp. It was beset by jealousies, political backhanders, corruption and influence peddling, and saddled with favourites as incompetent officers who at best were ’very weak” and had “no power or habit of command.”2 Hughes determined to ensure that no Regular soldier received a command appointment, and instead put in his favourites. These were drawn from the citizen militia, whose ability was summed up by the young iconoclast and future military theorist J.F.C. Fuller, who remarked that the Canadians had potential only “if the officers could all be shot.”3 Yet the Canadian Expeditionary Force (CEF) rose above this administrative nightmare, even if its impact continued to haunt the force for most of its existence.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.046 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".