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Record W1571422660

Ragnok in Mary Shelley’s Frankenstein: The Revenge of the Hrimthursar

2011· article· en· W1571422660 on OpenAlexvenueno aff
Abigial Ruth Heiniger

Bibliographic record

Venue˜The œjournal of ecocriticism · 2011
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsMonsterFrost (temperature)HistoryLiteratureArt historyArtMeteorologyGeography
DOInot available

Abstract

fetched live from OpenAlex

In April 1815, a volcano on the Indonesian island of Tambora erupted, devastating that region and causing a major climate change: 1816 was known in across the Atlantic as the “year without a summer.” While many scholars have interpreted the notorious weather of that year as the catalyst for Mary Shelley’s Frankenstein, scholars are only beginning to examine the depth to which that weather penetrated her work. This paper explores the ways that Victor Frankenstein’s creature resembles a Norse weather monster, an Hrimthursar or a frost giant, and examines Shelley’s distinctive message about the trauma of “a year without a summer.”

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)0.003
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: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0120.023
Scholarly communication0.0050.005
Open science0.0000.004
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.079
GPT teacher head0.323
Teacher spread0.244 · 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
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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Same venue˜The œjournal of ecocriticismSame topicScience Education and PerceptionsFrench-language works237,207