War in Wôbanak: Environmental Histories of the French and Indian Wars, 1675-1763
Bibliographic record
Abstract
In “War in Wôbanak: Environmental Histories of the French and Indian Wars, 1675-1763,” I argue that a century of conflict fought in northeastern North America can be explained by understanding different perceptions and relationships brought to bear on the natural world by members of the Wabanaki Confederacy, officials and soldiers of the British Empire, and English (descended) settler colonists. In Wôbanak, the Dawnland, the first place the sun rises each day in North America, stretching across what most maps now call Maine, Vermont, New Hampshire, Quebec, and the Canadian Maritimes, the people of the Wabanaki Confederacy, the Abenaki, Penobscot, Passamaquoddy, Maliseet, and Mi’kmaq made their home. And for the better part of one hundred years, they defended that homeland in the face of colonial and imperial expansion. While colonists and imperial officials insisted that the natural world could be commodified dominated, and extracted, Wabanaki people saw a space teeming with life and with relationships. By viewing the roles trees, non-human animals, agriculture and placemaking, and even pathogens played in these conflicts—and how differing ecologies shaped and were in turn shaped by them—these conflicts appear as environmental events. With the ascendance of the British Empire and the end of this story, their victory is a pyrrhic one as they are subsumed by settler colonists whose own environmental logic set the stage for contemporary environmental disaster.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".