A Not-too-distant Mirror: The Talcott Commission (1840-43) and the Meaning of the Border
Bibliographic record
Abstract
“Who we are” has always been defined in part by “who we’re not,” and who we’re not is often symbolized by our borders. America’s edges, its international borders, have become a critical focus of identity politics and border security - keeping out Mexican migrants, Canadian drug smugglers and other fiends - grist for the Sunday morning news show mills. However, today’s Fortress America is hardly new; students who examine America’s mid-nineteenth-century rush to solidify its national borders would find that their ancestors made a similar equation. The ways they defined their borders reflected the ways they defined themselves. In 1840 Congress commissioned a survey of the disputed land and border between Maine and Canada. Under the director of Captain Andrew Talcott, the commission’s maps, diaries, field notes, reports and correspondence reflect the perceptions that borderlands were the places where cultural and physiographic differences between Americans and British North America were visible and palpable enough to sustain an international boundary. Talcott and his fellow expeditionists were cultural explorers who interpreted the northeastern border even as they sought to draw it. Their border, like ours today, was as much an idea as it was a real place and a mirror reflecting what mid-nineteenth-century Americans thought they were, and were not.
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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 | 0.001 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".