“News of Provisions Ahead”: Accommodation in a Wilderness Borderland during the American Invasion of Quebec, 1775
Bibliographic record
Abstract
Soon after the American Revolutionary War began, Colonel Benedict Arnold led an American invasion force from Maine into Quebec in an effort to capture the British province. The trek through the wilderness of western Maine did not go smoothly. This territory was a unique borderland area that was not inhabited by colonists as a frontier society, but instead remained a largely unsettled region still under the control of the Wabanakis. On the northern periphery of this borderland the Quebecois and Wabanakis supplied Arnold and his men with provisions, aid, and intelligence. It was the assistance of French habitants and Wabanakis in Quebec that saved the mission. Historians who have written about Arnold’s march through this borderland region have tended to view it as simply a heroic feat by the American force. Yet, both the natural and human environment of this borderland region played a significant role in the expedition’s near failure to escape the Maine wilderness and ultimately its success in reaching Quebec City. The author is a graduate student at the University in Maine, focusing on the environmental history of the American Revolution. He is the secretary of the Environmental Studies Coalition at the University of Maine, co-editor of the Khronikos blog and journal, and the webmaster of the Northeastern Atlantic Canada Environmental History Forum.
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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 | 0.001 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".