My Letter of Confession: Sara Jeannette Duncan's Late Imperial Rhetoric and Risk-Taking
Bibliographic record
Abstract
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.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".