‘A Ticklish Craft’: Viewing Britain’s empire from inside a birch-bark canoe in the eighteenth century
Bibliographic record
Abstract
At the end of the French and Indian War elements of the British Empire moved quickly into the western Great Lakes and central Canada in an effort to partake in and control the lucrative fur trade. To do this both the British army and fur traders adopted a piece of Native American technology: birch-bark canoes. What may have seemed like just an expedient tool for travelling from one point to another actually had far reaching implications for all parties involved. Archival research, material culture analysis, and geographic information systems data together demonstrate that the birch-bark canoe’s speed and efficiency both facilitated and frustrated fur traders and the army alike. For the army it meant a lack of control over traders looking to skirt imperial oversight. Conversely, the trader’s ability to go far beyond the government’s reach put them under the power of Native communities. Decisions about how much food or trade goods to pack, which waterways to take, where to trade, where to get the canoes, and how to stay alive were all influenced by the unique characteristics of this remarkable craft.
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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 | 0.011 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".