Bibliographic record
Abstract
In this paper, Sherrill Grace, Findley’s biographer, will examine her biographical practices in the context of Findley’s own memoir, Inside Memory, and his interest in creating fictional auto/biographers and auto/biography in several of his major novels (notably The Wars, Famous Last Words, The Telling of Lies, and The Piano Man’s Daughter). His fictional auto/biographers often use the same categories of document that Findley himself used—journals, diaries, archives—and this reality produces some fascinating challenges for a Findley biographer, not least the difficulty of separating fact from fiction, or, as Mauberley says in Famous Last Words, truth from lies. Like many writers, Findley kept journals all his life, and they are a key source of information for his biographer; however, his way of recording information and his creation of fictional journals means that a biographer (like the readers of his fictional auto/biographers) must tread carefully. While not a theoretical study of auto/biography, in this paper Grace will offer insights into the traps that lie in waiting for a biographer, especially when dealing with a biographee who is as self-conscious an auto/biographer as Findley.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".