Mapping the Great Lakes: The Somageography of Water and Land, 1615–1828
Bibliographic record
Abstract
Building on our earlier computational analysis of 237 digitized maps of the Great Lakes, we use maps and accounts of Lake Huron, bookended by those of Samuel de Champlain (1616) and Henry Bayfield (1828), to explore the concept of “somageography”: how first-hand experiences with natural environments are filtered and reconfigured by map-makers, rendering maps as irreducibly complex representations of particular human and environmental conditions. While maps of Lake Huron, in particular, may appear to become more accurate by the 1820s, Bayfield’s detailed charts are simply epistemologically frozen moments when the individual map was sketched, which were then months or years later reproduced as though they were an unproblematic, even ontological, representation of an environmentally dynamic and climatologically unstable region. Our computational approach paradoxically reinforces our sense that the methods for mapping the natural world during the long eighteenth century cannot be understood apart from observational and biophysical experience—somageography—in a dynamic landscape.
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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 | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".