Creating an Indian Enemy in the Borderlands: King Philip’s War in Maine, 1675-1678
Bibliographic record
Abstract
In the borderlands space between New England and Québec, the Wabanaki Indians had their own reasons for getting embroiled in a conflict that started in southern New England, King Philip’s War (1675-1678). This essay argues that, ironically, the English vision of a monolithic Indian enemy was the key to Wabanaki success in this war. The Wabanakis were a heterogeneous group when it came to the issue of fighting the English, with many eager to join the fight, others ambivalent, and still others against. The English of Massachusetts Bay and Maine, however, treated the entire Wabanaki population as united under a central authority, and they retaliated against any Wabanaki depredations as if all Wabanakis were geared for war. This blanket attitude toward the Indians, held by many Englishmen from Maine, New Hampshire, and Massachusetts Bay, would be self-fulfilling. By assuming all of the Wabanakis were armed for war, English leaders, soldiers, and settlers minimized overtures of peace, fell susceptible to rumor, and retaliated with violence against most of the Indians they encountered. By treating them all as hostile, the English gradually alienated so many different groups of Indians in Maine that they encouraged even the most pacific Wabanakis to join the war. However, that homogenization of the Indian enemy did not lead to the centralization of Indian warriors, as the Wabanakis remained decentralized. Because the Wabanakis had no central army, they did not make a central target, and such diffusion would be critical to their victory in the Maine borderlands. The author is an assistant professor of History at Dickinson College. He researches the history of American Indian-European interaction during the colonial period in the northeastern borderlands.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".