Bibliographic record
Abstract
Introducing Margaret Atwood In November 2004 Margaret Atwood and Dame Gillian Beer engaged in a public conversation about her writing at the British Academy in London, a very “Establishment” literary event, where they discussed the image of the labyrinth as an appropriate description of the processes of writing novels and reading them. Two months later, Atwood appeared on a popular Canadian television show, rigged out in full ice hockey gear, showing the host, Richard Mercer, how to deflect a puck in Canada's favorite national sport. These two images of Atwood, as internationally famous writer talking seriously with a Cambridge professor about the mysteries of her craft, and the other as Canadian celebrity advertising her national identity in a playful masquerade, illustrates the combination of high seriousness and witty ironic vision which is the hallmark of Atwood's literary production. In this book, our primary concern is with Margaret Atwood the writer, but there is also Atwood the literary celebrity, media star, and public performer, Atwood the cultural critic, social historian, environmentalist, and human rights spokeswoman, and Atwood the political satirist and cartoonist. The chapters in this volume address all these features in the Atwood profile, as they consider her career from a variety of perspectives and with very different emphases, though it is her Canadianness and her international appeal as an imaginative writer which are the two leitmotifs .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.512 | 0.336 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".