Traces of an Arctic Voice: The Portrait of Qalaherriaq
Bibliographic record
Abstract
This article analyses the portrait of the young Inughuit hunter Qalaherriaq, who was brought involuntarily to England from his home in Perlernerit (Cape York) in today's Kalaallit Nunaat (also known as Greenland) with Captain Erasmus Ommanney’s expedition vessel in 1851. The portrait’s highly unconventional representation, wherein the sitter is shown both en face and in profile, betrays an interest in nineteenth-century racial science and civilizing ideologies. Despite this problematic colonialist content, the double portrait serves as a record for the existence and experience of Qalaherriaq and the participation of Inuit individuals in European expeditions to the Arctic. As this article argues, the portrait is also a visual testimony to Qalaherriaq’s agency, adaptability, and deliberate performance in a social environment characterized by ethnocentrism and racism. Bringing in the trail of Inughuit and European sources that this portrait connects to, this article traces the nature and terms of Qalaherriaq’s stay in British society. As a decolonizing strategy, we use the method of concurrences to avoid universalizing perspectives on the past. Examining moments of competing truth claims in the European and Arctic sources about or relating to Qalaherriaq, we point to the competing perspectives on the Arctic, exploration, and British imperialism contained in this material.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".