Landscape and Vision in Gretel Ehrlich’s This Cold Heaven: Seven Seasons in Greenland
Bibliographic record
Abstract
Depicting the narrator’s repeated travels to the northwestern coast of Greenland, Gretel Ehrlich’s This Cold Heaven aims to portray the landscapes of Greenland in a way that frees them from the constraints of the visual ideology associated with Western culture’s idea of landscape. This, however, is no easy task in a natural environment dominated by wide and grand views that seem to invite the detached observer’s ordering vision. This article shows how Ehrlich’s text uses Inuit narratives and ontologies that share perspectives with feminist theories on space and subjectivity in order to challenge our Western modern culture’s conceptions of vision and landscape. The narrator’s experiences of dogsled travel in landscapes determined by weather, ice and light conditions create novel sensations that display and disrupt the boundaries of the physical environment as well as of Western conception of the subject. In this manner Ehrlich’s travel narrative gradually develops away from a rationalist and objectifying form of geography towards a different and more embodied perception of landscape that acknowledges the relational and dynamic nature of Greenland’s icescapes. This rewriting of landscape implies an understanding of vision as an integral part of a bodily whole, in constant interaction – or even co-constitution – with the environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".