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Record W2581774225

How to Be a Heroic Explorer in a Friendly Arctic: A Chronotopic Approach to Self-Representation in Vilhjalmur Stefansson’s The Friendly Arctic: The Story of Five Years in Polar Regions (1921)

2017· dissertation· en· W2581774225 on OpenAlexaboutno aff
Silje Gaupseth

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2017
Typedissertation
Languageen
FieldArts and Humanities
TopicNarrative Theory and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsArcticPolarEnvironmentally friendlyThe arcticRepresentation (politics)OceanographyPhysicsAstronomyGeologyEcologyBiologyLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This dissertation deals with the exploration account The Friendly Arctic: The Story of Five Years in Polar Regions (1921), written by Canadian-American anthropologist and explorer Vilhjalmur Stefansson (1879–1962). Stefansson’s story is based on his experiences during the Canadian Arctic Expedition, which traversed and mapped stretches of ocean and land in the Canadian Arctic in the years 1913–1918. The methodological and theoretical approach of the study is largely based on Mikhail Bakthin’s concept of the chronotope, which is combined with relevant concepts and analytical approaches from narrative theory and method. In order to understand Stefansson’s narrative self-representation in The Friendly Arctic, the study contends, two interdependent—and potentially conflicting—chronotopes that give form to his narrative must be examined: a friendly Arctic chronotope and a quest chronotope, which combine elements of plot and character, story and discourse. Against this background, the self-representation of Stefansson as Arctic explorer (basing his characteristic explorative techniques on Inuit knowledge) may sometimes be seen as ambivalent, and there is a similar tension in the narrative representation of his friendly Arctic. The study is of relevance to the field of travel and exploration literature, and is influenced by recent work on Arctic discourses.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.285
Teacher spread0.230 · 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 designQualitative
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

Citations1
Published2017
Admission routes1
Has abstractyes

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