A Portrait of Raymond Brutinel as a Young Man (Part II): The Future Canadian Corps Machine Gun Commander as a Business Entrepreneur in the Canadian West, 1908â1914
Bibliographic record
Abstract
The following carries on from an article on Brutinel’s prewar life in Edmonton, Alberta that appeared in the previous issue of Canadian Military History. That account dealt with his arrival in Edmonton from France, the reasons for his immigration, and his adaptation to life in the newly-created Alberta capital. This included an initial involvement with the Edmonton French community, his editorship of the French language Le Courrier de l’Ouest, and his eventual breaking away from these pursuits into a career of business entrepreneurship. The following is specifically concerned with this latter phase of his career, in which, at the height of the ‘Laurier boom,’ he enjoyed great success. Included are his role as an agent for a syndicate of wealthy Montreal capitalists, his work as an explorer for coal deposits, and his promotion of numerous community development schemes, intended both to assist with community improvement and to earn money for his Montreal backers. These are recounted to clarify for the first time the kinds of activities that preoccupied Brutinel before the war and to help to illustrate his experiences and the capacities he developed and subsequently brought to his service as an officer with the Canadian Corps on the Western Front.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".