Bibliographic record
Abstract
Incest autobiography is a distinct form of trauma writing. Although the sub-genre began more than two decades ago, it remains largely unexamined. Canadian writers’ substantial contribution to incest autobiography, in particular, is not well recognized. Four of the Canadian incest autobiographers, Charlotte Vale Allen, Sylvia Fraser, Elly Danica, and Janice Williamson have written specifically about father-daughter incest. Their books, Daddy’s Girl (1980), My Father’s House (1987), Don’t (1988), and Crybaby! (1998), because they are similar in focus but different in style, demonstrate the many ways to write autobiographically about incest. They validate incest victims’ experience while demonstrating that incest trauma is not monolithic. They are also shocking. Despite the media’s continuous exposure of incest victimization, most people will not feel a sense of complacency or familiarity with the presentation of incest in the autobiographies. Because of the very intimate autobiographical voice and the steady focus on incest, the books elicit an empathetic response. Readers become the witnesses to the incestuous abuse. Allen Fraser, Danica, and Williamson have encouraged an engaged reading of their books. They have also extended the parameters of contemporary Canadian women’s writing, autobiographical writing, and writing about sexual violence against children and women. The now well developed sub-genre of incest autobiography is in a position to make a substantial social and literary impact.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.017 |
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".