Bibliographic record
Abstract
Indians and British Outposts in Eighteenth-Century America is an intriguing examination of five British forts and the roles each filled along the early American frontier during the era of the Seven Years' War. Each of the five forts—Fort Loudoun at the intersection of the Tellico and Little Tennessee Rivers on the western North Carolina border, Fort Allen on the Lehigh River in eastern Pennsylvania, Fort Michilimackinac on the Straits of Mackinac joining Lakes Huron and Michigan, Fort Niagara overlooking the drainage of the Niagara River into Lake Ontario, and Fort Chartres on the Mississippi River in the Illinois Country—occupied an important place on the mid-eighteenth-century North American landscape. Daniel Ingram demonstrates that these outposts were far more than just bastions of British power on the frontier. They were contact points, zones of interaction, and multicultural communities whose existence and function depended as much on Indians as they did on the British or their colonists. Indeed, the focal point of Ingram's analysis is the impact and influence that Indian peoples had on the British posts and the varying strategies and mechanisms that native peoples employed to incorporate the forts into their daily lives. Ultimately, Ingram concludes that Indians maintained “considerable effectiveness in defining fort-based cultural relations and determining the means of life and death in the backcountry” (p. 26).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".