The doctor and the murderess : a discussion of knowledge and ignorance in margaret atwood's alias grace
Bibliographic record
Abstract
Grace Marks was a convicted double murderer in nineteenth-century Canada. Her case was well known at the time thanks to its sensationally violent and sexual details. The novel Alias Grace (1997) by Margaret Atwood engages in a discussion about the relationship between fact and fiction, scientific objectivity and power. This article analyses the relationship between Atwood’s fictional Grace Marks and Dr Simon Jordan, an American doctor who visits her in prison hoping to find out the truth about Grace and the murders. Both Grace and Dr Jordan are formed by the existing norms of the time period, norms which govern how men and women of their particular class should act. However, what makes their meetings noteworthy is that Grace Marks possesses knowledge of the norms and expectations and can therefore use them to her advantage, whereas Dr Jordan does not, despite being an educated and professional man. In the end, this leads to Grace’s ability to tell her own story, and Dr Jordan’s failure as a man of science.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.046 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".