How the Arctic Became White: Qallunaat Explorers’ Misrepresentations of the Botanic Landscape
Bibliographic record
Abstract
On account of its geographic remoteness from southern Canada and Europe, the Arctic region has long been consumed and mediated by images and media, yet until now, little scholarly attention has been given to explorers’ sketches, prints, and other disseminated visual culture. This thesis investigates the historic roots of the perception that the Arctic landscape is a "flat, white nothingness." I ask how and why explorers throughout the nineteenth to the early twentieth centuries represented the Canadian-Alaskan Arctic as devoid of flora, as they often visited in the summer months when the land is covered in mosses, lichens, flowers, and other colourful plant life, and actively gathered botanical samples on these same expeditions. In this thesis I argue that Qallunaat explorers deliberately misrepresented the Arctic environment to bolster their own accomplishments and supposed technological superiority, despite having to continuously rely on Indigenous technologies and knowledge of the land for survival. Colonial explorers’ images are generally variations on the theme of ice and snow, oversimplifying a complex natural order. These landscape representations replace a focus on the natural environment with a focus on the explorer “exploring”. In this thesis, I demonstrate how Inuit artists challenge these outsider narratives by foregrounding their botanical knowledge and reasserting their own representations of their home land, Inuit Nunangat, through contemporary art practices. I read the land's agency, Inuit knowledge, and environmental art history back into this dominant discourse of frozen imagery. This thesis addresses how we construct and consume images of the natural world, which landscapes we deem important or aesthetically pleasing to conserve, and what others we designate to be sacrificed for industry. This is crucial to the polar region, a place that climate change is rendering increasingly important in global politics and economics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".