The Sea of Ice and the Icy Sea: The Arctic Frame of <i>Frankenstein</i>
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".