Bibliographic record
Abstract
Implicating epistolary strategies for misconception and doubt is taken further by Margaret Atwood, whose ‘fictional excursion’ into the ‘real Canadian past’ exposes contradictions in the story of an infamous nineteenth-century murderess. 3 Neo-Victorian fiction perpetuates the Victorian sensation novel’s parasitical relationship with crime reporting by re-employing and imagining documents that litigate with history. Identified as one of a number of ‘typical neo-Victorian narratives based on true crime’, 4 Alias Grace (1996) is a pastiche of Grace Marks’s story which demonstrates that ‘the past is made of paper’, but the historical record is confusing and contradictory. 5 The novel pieces epistolary-style narratives into a patterned patchwork of voices that jostle discordantly side by side in search of narrative authority. This includes a secret diary-style voice that fosters ideas of deception and ambiguity to deny resolution for enduring questions of guilt or innocence, thereby illustrating that ‘a murderess is not an everyday thing’. 6 Atwood claims that Grace’s story ‘is a real study in how the perception of reality is shaped’; voice and the diverse roles of writer and critic are therefore key preoccupations for Atwood as she debates processes that effectively effaced Grace’s legibility. 7 This chapter will consider whether a deviant diary style permits Grace Marks to become primarily an alias for Margaret Atwood to deliver her authorial polemic. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| 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".