Mapping Champlain's Travels: Restorative Techniques for Historical Cartography
Bibliographic record
Abstract
Samuel de Champlain's travels through what would become New France have been extensively documented and mapped by geographers and historians today. As conventional cartographic depictions of the routes of a European explorer and colonizer, these maps portray the locational details of Champlain's journeys but omit the emotional geographies and the sense of place evoked in his journals, as well as the Indigenous geographies interwoven with Champlain's story. This article suggests techniques for restoring multiple experiences and multiple voices to the historical cartography of Champlain's travels, including the expressive use of colour and type, the blending of spatial and temporal scales in sequential insets, the incorporation of mental maps and dream geographies, and the representation of Native voices through place names and imagined dialogue. In so doing, the authors reimagine historical cartography for the representation of place rather than space by taking a narrative approach to cartographic language.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".