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Record W7030467670

Mutiny Echoes: India, Britons, And Charles Dickens's A 'tale Of Two Cities'

2007· article· en· W7030467670 on OpenAlexvenueno aff

Bibliographic record

VenueSound Ideas (University of Puget Sound) · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsnot available
Fundersnot available
KeywordsMutinyDisenchantmentChoseIdentity (music)National identityRidiculousWishThatcherism
DOInot available

Abstract

fetched live from OpenAlex

This essay asks what, if any, import the Indian "Mutiny" of 1857 had on A Tale of Two Cities (1859), Charles Dickens's fictionalized account of the French Revolution. Begun shortly after the Indian uprising started, Dickens's historical novel appears studiously to avoid any mention of events on the Indian subcontinent, even though these events preoccupied and enraged the author. Few scholars have attended to the question of A Tale of Two Cities and the "Mutiny," but when they have, scholars have looked for analogies between India and Dickens's account of the French Revolution. In this essay, by contrast, I examine A Tale of Two Cities in a larger context-of Britons' response to the Uprising, of Dickens's short stories and essays in Household Words in the years before the "Mutiny" and immediately after, of Dickens's disenchantment with aspects of British culture, and of his need to articulate a national identity grounded in action. I argue that the events in India were the match that ignited Dickens's already established mid-century interests in national identity, nobility, and masculine heroism. I do not wish to Suggest that A Tale of Two Cities is an Indian "Mutiny" novel, but rather that it is a novel about the "Making of Britons," an important endeavor for an author who was intensely dissatisfied with the Britain that he saw around him.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 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

Citations0
Published2007
Admission routes1
Has abstractyes

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