The Science of Territorial Domination in General Haldimand’s Defence of Quebec, 1778-1783
Bibliographic record
Abstract
In General Haldimand’s little-studied administration of Quebec during the American Revolutionary War, military strategy depended upon gathering information about the natural environment. Haldimand preserved Quebec for the British not by force, but by applying continental modes of territorial domination. Rather than secure the St. Lawrence Valley in an intimidating show of military force, Haldimand sought to secure the vitality of the fur trade along the Great Lakes corridor. This endeavor required Haldimand to look for the natural laws that created unity out of the social and geographic territory he had to defend, and to protect the most vital links: the economic currents and the transportation system. Thus, the Royal Engineers took precedence over other military officers as they collected a large body of information about the natural environment of the Great Lakes region. They drew maps, sounded bodies of water, and made meteorological observations, turning pleasant bays into safe harbors. The knowledge gathered replaced Mississaugan perspectives of the land, revised French information and set the agenda for Loyalist settlement in the region. This paper however, focuses upon Haldimand’s role in applying continental attitudes towards the landscape that helped solidify the link between natural history and imperialism of late-18th century Britain.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".