"If you look long enough, eventually you will see me": The Power of the Elusive in Atwood's Alias Grace
Bibliographic record
Abstract
Throughout her long career, Canadian poet, novelist and critic Margaret Atwood has known for her incisive depictions of the patriarchal subjugation of women. Atwood's acclaimed novel Alias Grace, based on this historical Grace Marks, a young servant accused of murder in the mid 1800s, employs a particular technique also seen in her poetry and short stories, particularly “Death by Landscape” in the Wilderness Tips collection. In each, a female character is elusive, and knowledge of her is necessarily fragmentary. In “Death by Landscape”, a young girl disappears in the woods yet is deemed to be “fully alive” in landscapes paintings that call to mind the setting in which she vanished. In “Isis in Darkness”, a story in the same collection, a young man becomes reconciled to his role in the life of the woman he loved, acting as an 'archaeologist' and putting together fragments of her life. Knowing or even seeing the whole woman is impossible, but this offers power, protection and immortality to these subjects, who thus avoid the societal gaze. Alias Grace represents Atwood's fullest depiction of this elusive female.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.021 | 0.026 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".