Qallunology of an Arctic Whaling Encounter: An Inuk’s Transatlantic Voyage, 1839 to 1840
Bibliographic record
Abstract
This thesis borrows the analytical framework of Qallunology to examine a nineteenth-century Arctic whaling encounter between Scottish whalers and an Inuk geographer: Inulluapik. This thesis analyzes the narrative, written by Scottish surgeon Alexander M’Donald, of Inulluapik’s transatlantic journey to Aberdeen, Scotland and Tinnujivik (Cumberland Sound) from 1839 to 1840. I show how Inulluapik’s experience in Aberdeen in 1839, as recorded by M’Donald, provides insight into early Victorian worldviews and perceptions, which I call M’Donald’s Qallunaat-dom and Qallunaat-ness. By conducting a Qallunology of M’Donald’s description of the historical episode, I examine his early Victorian Qallunaat-dom, which compared Inuit from the eastern Arctic to Scots in Aberdeen through his binary understanding of whaling, gender, and spirituality. M’Donald’s interpretation of Inulluapik’s experience demonstrated his contrasting views of Inuit and non-Inuit cultures, which intersected with early Victorian ideas of civilization, intelligence, behaviour, appearance, respectability, female domesticity and marital purity, and Indigenous authenticity. In contrast, Inulluapik demonstrated fluid resistance to M’Donald’s early Victorian binaries of subsistence versus commercial whaling, rural versus urban, primitive versus advanced, and uncivilized versus civilized, and Indigenous versus non-Indigenous.
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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 | 0.002 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".