Negotiations of Empire: Rooting out the American Citizenry in the Borderlands of Upper Canada, 1805-1820
Bibliographic record
Abstract
This research examines the negotiations that transpired between the people, the British imperial government, and the land within the Detroit River borderlands between 1805 to 1820. This work marries borderlands and imperial interpretations and forms a cohesive foundation for analysis, which interprets empire as a framework through which the people of this region maneuvered. Reciprocally, within this negotiated process the people themselves become a mechanism of empire. Therefore, this work amends a historiographical gap within the Detroit-Essex borderlands that often divides imperial and cultural methods. Focusing primarily on the years surrounding the War of 1812, this work draws nuanced connections between empire, land, and community formation specifically in Essex County, Ontario. Partly through its outright destruction, this imperial conflict drew both Detroit and Essex County closer into the orbits of the opposing metropoles thus challenging the resiliency of the woven kinship networks that spanned across the Riverlands community. This work considers the burgeoning free Black communities that emerged during the first half of the nineteenth century in Essex County and the correlation therein between freedom and the war itself. Ultimately, under the strain of empire, the community land matrix of the region was forever altered, while the personal relationships across the strait prevailed.
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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 | 0.002 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".