Bibliographic record
Abstract
This chapter recounts the most important operations in North America: the capture of Louisbourg, Quebec and Montreal, and with it, the elimination of a French presence in Canada. It first discusses the promotion of James Wolfe to brigadier and his deployment to America on the advice of General Sir John Ligonier, whom William Pitt, as the new prime minister, had asked to replace Cumberland as captain-general of the British Army. The chapter also elaborates on the promotion of Jeffrey Amherst to major general to command the expedition, mentioning that Wolfe was too junior for overall command. Amherst and Wolfe had very different views on the planning and prosecution of war. Where Amherst saw the need for caution, good intelligence and detailed planning of operations to bring about battle in optimal circumstances, Wolfe's self-education and military experiences had inculcated the requirement for decision and action. As the chapter emphasizes, this subtle but crucial difference in approach would define the campaign to conquer New France.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".