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Anna Karenina i Arktis: Tekst og landskapi Aritha van Herks <i>Places Far From Ellesmere</i>

2018· article· no· W2802873246 on OpenAlexaboutno aff
Janicke Stensvaag Kaasa

Bibliographic record

VenueNorsk litteraturvitenskapelig tidsskrift · 2018
Typearticle
Languageno
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Anna Karenina in the Arctic: Text and Landscape in Aritha van Herk’s Places Far From Ellesmere The Arctic is an imagined space as much as a geographical place; our ideas and understanding of the region are based both in the fictional and the factual. Aritha van Herk’s Places Far From Ellesmere (1990) combines multiple genres in its experimental rendering of the author’s journey to the Canadian north, thus touching upon the issues of genre and the boundaries, or rather the lack of such boundaries, between the fictional and the factual in the literature of the Arctic. The article examines how van Herk’s text contests the genre conventions of exploration narratives through the traveller’s self-representation and by making Anna Karenina a key text in the account of her journey. These features, the article argues, are decisive to van Herk’s feminist critique of Tolstoy’s novel, as well as of exploration writing on the Arctic generally, and also point to the recurring issue of fact and fiction in the literature of the Arctic. Keywords Arctic literature exploration writing Aritha van Herk Places Far From Ellesmere genre conventions

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.002

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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