A Map of Divergence and Connection: Voices from Nineteenth-century Nunavut and Aberdeen
Bibliographic record
Abstract
This essay explores interactions between five people – Inuit, Scottish, and American – in Nunavut and Aberdeen, Scotland, between the late 1830s and the 1860s. The fact that these people all knew each other, or of each other, provides an opportunity to investigate a rich network of communication between them over a twenty-year period. The main primary sources investigated include the first biography of a young Inuk, written by a Scottish doctor; an American explorer’s tale of his Life with the Esquimaux; and the personal journal of a Scottish whaling captain’s wife. The investigation focuses on five subjects which occur persistently across these works: blood, mapping, tea, maktaaq and other country food, and the act of leave-taking. The topics form nodes, sometimes of incommensurability between Inuit and Qallunaat (an Inuktitut word designating non-Inuit, usually white, people) but they can also become areas through which misunderstanding and separation give way to understanding and close bonds. The fact that in these sources Inuit voices are ventriloquized by white Scottish and American writers greatly increases the risk that Inuit historical voices could be misrepresented and misheard. As a non-Inuit scholar, I am acutely aware that I may mishear or misunderstand these voices myself. In my analyses of the source material, I can bring the close attention of a scholar of nineteenth-century literature to attend to tone, ambiguity, the historical period, genre, and also to the occlusions and confusion in the writing that may obscure (often unconsciously) the redaction of veritable, if not entirely verifiable, Inuit voices from the nineteenth century. But I may be wrong, and this uncertainty is fundamental to my methodology. The expectation of scholarly writing in the academy is that it will be presented in an authoritative voice, but my methodology while listening for historical Inuit voices in the chosen sources must entail uncertainty. Research in the following essay is an open letter to other scholars, and especially to indigenous scholars and readers, who may agree or disagree with what I hear and interpret of Inuit voices in some lesser-known works of the mid-nineteenth-century historical record.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.035 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".