Bibliographic record
Abstract
Scholarship on Margaret Atwood’s novel Alias Grace (1996), according to the scholar Gina Wisker, has principally taken two directions: ‘historical […] contextualiz[ations of] the representation and treatment of women’ and ‘problematiz[ations of the] ways in which people and historical records are obsessed with the impossible task of fixing, articulating, proving history, and the facts of any events’. Drawing on Caroline Levine’s influential work on form, and focusing on the first episode of Mary Harron’s six-episode adaptation (2017), I argue that the miniseries routinely tantalizes us as a whodunnit, but that it, in fact, reflects back to us our own needs and wants. I go over Atwood’s and Harron’s source material before revealing how they characteristically point to the inherent uncertainties of Grace Marks’s case and how, in so doing, they do significant service to the complexities of her character. My broader claim is methodological: my reading speaks to the two registers identified by Wisker, and it reveals how Harron builds on Atwood by provoking us both to make intentional our partiality as viewers and to situate ourselves in our interpretative projects.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".