Bibliographic record
Abstract
<p>[para. 1]: "In 1923, after nearly a decade of diary keeping, the Cuban-French-American author Anaïs Nin (1903– 77) recorded in her journal, “Someday I want to write about this, as a tribute to a much despised form of literature, as an answer to those who have shrugged their shoulders when they saw me bending over a mere diary. I shall try to give diary writing a definite character and a definite place in life, and for the sake of the practical people who have wept over the wasted hours, I shall demonstrate the uses, the purpose, the visibly beneficial effects, of the much deplored habit.” 1 While a plethora of nonliterary authors have gained a literary reputation through the publication of their journals— for example, peeress Lady Anne Clifford (1590– 1676) and naval administrator Samuel Pepys (1633– 1703)—this chapter focuses on professional creative writers in English in Britain, the United States, and Canada whose diaries have also been published. In particular, I analyze the diaries of Nin as well as Virginia Woolf (1882– 1941), Alice Dunbar-Nelson (1875– 1935), John Cheever (1912– 82), and George Fetherling (1949–) as exemplary twentieth- and twenty-first-century practitioners of the genre.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".