Measuring Ground: Surveyors and the Properties of States in the Great Lakes Region, 1783-1840
Bibliographic record
Abstract
Between the end of the America Revolution and the middle of the nineteenth century, surveyors materialized landed property and settler states in the Great Lakes region. At the end of the eighteenth century, British and American officials faced a similar dilemma: both aimed to transform Indigenous homelands across the region into property for settlers. Yet neither initially possessed either the capacity or legitimacy to achieve this end. This dissertation is the first in-depth study of the two offices that eventually fulfilled this objective: the Surveyor General’s Office for the colony of Upper Canada and the Surveyor General’s Office responsible for the American Territory of Michigan. Contests over boundaries materialized by surveyors’ fieldwork and paperwork not only stressed the material foundations of state and property formation but complicate understandings of property as an abstract bundle of rights. Crown officials for Upper Canada and their federal counterparts for the Territory of Michigan recognized that the security of property and the authority of the state hinged on the certainty of survey boundaries. It required a standard that mathematics could not supply. Efforts to provide that certainty thus required each state to pass regulations that determined how surveyors measured land and how each office organized its documentary archive. It also required surveyors to solicit support from local elites, ordinary settlers, and even Indigenous nations, a dynamic most visible when surveyors adjusted disputed boundaries through collective agreement. This dissertation argues that the shape of the state and the nature of property turned on the spatial order generated surveyors’ during this period. It finds striking parallels between the federal and Upper Canadian states’ reliance on surveys to territorialize jurisdiction, administer land markets, and ensure public order, especially amidst major demographic shifts after the War of 1812. By the late 1830s, after Michigan had become a state and rebellion rocked Upper Canada, the authority of Crown and federal surveyors in this process began to diminish. Courts increasingly came to settle boundary disputes, a shift that made it possible to see property as increasingly untethered from surveyors’ infrastructure of maps and markers. Surveyors changed the Great Lakes region, only to change in turn.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".