Bibliographic record
Abstract
In the nineteenth and early twentieth centuries, a stream of popular narratives celebrated the struggles of European and American explorers who pushed out to the edges of their known worlds. Many of these adventurers travelled through Inuit homelands in the North American Arctic, recording their surroundings as inherently forbidding and desolate. These explorers are part of an arctic survival mythology that extends much further and deeper. In this environmental and cultural history, I consider lesser-known survival narratives drawn from oral histories and archival sources, namely stories of American whalers in Inuit territory, Inuit families in the United States, American and Inuit polar expedition members, and Inuit who remained in their homeland as it changed around them. I compare the strategies these individuals employed to survive physically, psychologically, and culturally when they faced hardships such as starvation, malnutrition, and disease. My four chapters are structured around different ways of marking ecological and social time, and they are centred on the rich maritime region of Cumberland Sound on Baffin Island, in what is now Nunavut, Canada. I argue that Inuit and Americans often saw each other’s latitudes as inhospitable, and that divergent cosmologies shaped their perceptions of unfamiliar sites. Together, these unconventional arctic narratives demonstrate that the definition of a harsh environment is relative, and they offer alternative ways of thinking about individual and cultural survival.
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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 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".