Bibliographic record
Abstract
The Winnifred Eaton Archive (WEA) is an accessible, fully searchable, digital scholarly edition of the collected works of Winnifred Eaton Babcock Reeve , best known for the popular Japanese romances she signed "Onoto Watanna."Born in Canada and establishing her career with the publication of the best-selling novel Miss Nume of Japan, Eaton was the first Asian North American novelist and achieved sensational success in her lifetime.She was also a self-supporting journalist, playwright, and screenwriter.Her works were translated into scores of languages, reprinted, and adapted for the stage and screen.The Winnifred Eaton Archive is organized into exhibits that correspond to periods in Eaton's career.These periods overlap chronologically: Early Experiments features texts written in the 1890s and early 1900s during Eaton's writing apprenticeship in Montreal and Jamaica, and/or before she had taken up her "Japanese" identity as "Onoto Watanna"; Playing Japanese collects texts written on Japanese subjects and themes from 1896 until 1922; New York Years collects texts from a period of reinvention (1901)(1902)(1903)(1904)(1905)(1906)(1907)(1908)(1909)(1910)(1911)(1912)(1913)(1914)(1915)(1916) after the novelty of Eaton's Japanese romances had faded, when Eaton tried writing dialect fiction and autobiographically inspired novels; Alberta (still incomplete) will collect texts written about Western ranch country and advocacy for Canadian
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.051 |
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".