Bibliographic record
Abstract
Margaret Atwood has been writing poetry, fiction and criticism for almost fifty years. Her influence on Canadian literature is phenomenal, and her influence on contemporary literature as a whole is immense. Her readings fill theatres and her books win a range of literary and social prizes. She has gone from being ‘world famous in Canada’ (to repeat Mordecai Richler's famous joke) to being world famous, full stop. Atwood used to find that the media tried to reinvent her in ways that she didn't recognize, and perhaps some of that reinvention continues. However, Atwood notes: Once you hit the granny age, people think that you may be okay and that you're handing out cookies to younger writers and waving your benevolent fairy godmother wand over the proceedings, but you're no longer the sort of threat that you were because people kind of know what you are by now. They're not expecting some awful threatening surprise to appear. Yet Atwood continues to have the power to surprise – from embracing new genres, to developing expertise in the extra-textual side of contemporary publishing, to returning to the poetry that first made her famous. Each Atwood text is a treat, whether it spans only a few lines, or offers up an intricate puzzle in the form of a multilayered novel. Spanning different genres, as well as crossing over them, Atwood's work appeals to academics and non-academics alike, and this introduction will give you the opportunity to explore not only her own life and work but also the contexts for it and reception of it.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.432 | 0.244 |
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".