Bibliographic record
Abstract
D " " is often taken as an insult: a dismissal of the book on the grounds that its primary, and possibly only, appeal is at the level of plot.But all fi ction attempts to appeal to its readers, and those readers should be tempted to turn the pages.In the case of Margaret Atwood's fi ction, however, readers are tempted to turn the pages both ways.Her fi ction urges fi rst-time readers forward, forward toward richly satisfying, if not entirely conclusive, moments of closure.But her fi ction also demands readers to turn backwards, to turn the pages in the other direction as well: to go back to read again and reassess in light of the new insights they have gleaned as they have read forward. ink, for instance, of the diff erence between a reader's fi rst encounter with Off red in e Handmaid's Tale and that same reader's return to the novel once s/he understands that Off red's story has been pieced together by the insidious Pieixoto.Similarly, with each new addition to the œuvre of this prolifi c author, while readers fi nd themselves moving on to meet new fi ctional characters and landscapes, they also fi nd themselves returning, turning back as it were, to earlier Atwood works in order to read those works through a new lens and with new insights.Take, for example, Blind Assassin, which,
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".