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Record W2751185283 · doi:10.14430/arctic4669

The Sea of Ice and the Icy Sea: The Arctic Frame of <i>Frankenstein</i>

2017· article· en· W2751185283 on OpenAlexvenueno aff
Janice Cavell

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsArcticNarrativeHistoryThe arcticFraming (construction)NewspaperOceanographyLiteratureArtArchaeologyGeologyMedia studiesSociology

Abstract

fetched live from OpenAlex

It has become common for scholars to understand the Arctic framing narrative of Mary Shelley’s Frankenstein as a commentary on the northern expeditions sent out by the British Admiralty after the Napoleonic Wars. According to this view, the character Robert Walton is a surrogate for John Barrow, the principal organizer of the Admiralty expeditions. This article demonstrates that chronological factors make such an interpretation untenable. Yet the process through which the far North became the setting for Frankenstein’s opening and closing scenes is of great importance for understanding the evolution of the novel into its final complex form and with regard to broader considerations about the Arctic’s place in Romantic literary culture. The article suggests other sources for the Arctic frame, most notably the 1815 plan by whaler William Scoresby for a sledge expedition toward the North Pole. Although Scoresby’s lecture was not published until 1818, reports appeared in newspapers and periodicals soon after the lecture was given. There is strong circumstantial evidence to suggest that Mary Shelley read these reports. By tracing the likely influence of Scoresby and other Arctic writers on Frankenstein, the article both sheds new light on the novel itself and demonstrates the extent of the Arctic’s presence in European culture even before the famous Admiralty expeditions.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.080

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 studies0.0100.020
Scholarly communication0.0070.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.341
Teacher spread0.305 · 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 designTheoretical or conceptual
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

Citations3
Published2017
Admission routes1
Has abstractyes

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Same venueARCTICSame topicScience Education and PerceptionsFrench-language works237,207