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Record W1591983453 · doi:10.7557/13.3436

Landscape and Vision in Gretel Ehrlich’s This Cold Heaven: Seven Seasons in Greenland

2015· article· en· W1591983453 on OpenAlexaboutno aff
Sigfrid Kjeldaas

Bibliographic record

VenueNordlit · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsHeavenNarrativeIdeologyStyle (visual arts)AestheticsSubjectivityHistoryLiteratureArtPhilosophyEpistemologyLaw

Abstract

fetched live from OpenAlex

<p align="left">Depicting the narrator’s repeated travels to the northwestern coast of Greenland, Gretel Ehrlich’s <span style="font-family: TimesNewRomanPSMT;"><em><span style="font-family: TimesNewRomanPS-ItalicMT;">This Cold Heaven </span></em><span style="font-family: TimesNewRomanPSMT;">aims to portray the landscapes of Greenland in a </span></span>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.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.345
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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