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Record W1988240779 · doi:10.3138/utq.84.1.1

My Letter of Confession: Sara Jeannette Duncan's Late Imperial Rhetoric and Risk-Taking

2015· article· en· W1988240779 on OpenAlexvenueaboutno aff
Shelley Hulan

Bibliographic record

VenueUniversity of Toronto Quarterly · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature, Film, and Journalism Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRhetoricAnecdoteRhetorical questionPersonaJournalismHistoryArgument (complex analysis)Confession (law)EmpireSociologyMedia studiesPanopticonHierarchyLiteratureArt historyLawArtHumanitiesPhilosophyPolitical scienceAnthropologyBrotherLinguistics

Abstract

fetched live from OpenAlex

Canadian writer Sara Jeannette Duncan (1861–1922) found early success as a journalist in North America, writing most of her novels after she migrated to India and England in 1890. Janice Fiamengo's argument that the young Duncan developed an “insouciant public voice” in the ephemeral press invites inquiry into whether, and to what ends, she cultivated that voice after her move. A newly-discovered collection of Duncan's letters shows that she continued to evolve the risk-taking persona of her early journalism, deploying it in this instance to enhance her relationship with India's Vicereine Mary Curzon. In this correspondence, Duncan transforms a satirical sketch she published pseudonymously in London's Daily Mail into an amusing anecdote that dramatizes her loyalty to the Vicereine. The letters thus reveal some of the rhetorical strategies that Duncan used to collapse the social hierarchy of the late British empire to her advantage while critiquing that same hierarchy.

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)0.008
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.247
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.009
Scholarly communication0.0070.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.196
Teacher spread0.181 · 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
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
Published2015
Admission routes2
Has abstractyes

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Same venueUniversity of Toronto QuarterlySame topicLiterature, Film, and Journalism AnalysisFrench-language works237,207