In her earlier book on Canadian literary celebrity, Literary Celebrity
Bibliographic record
Abstract
Atwood with a comment Atwood made in 1973: “I’ve been de-scribed as the Barbra Streisand of Can Lit … But I think of myself more as the Mary Pickford, spreading joy ” (99). As York mentions, Atwood’s obser-vation is both wry and self-aware. Mary Pickford, a Canadian actress who eventually became a Hollywood starlet during the silent era, made the transition from the mar-gins of her profession, far from the engines of international celebrity, to a central role as a prominent film producer, public benefactress, and founder of the United Artists film studio. Near the beginning of her own illustrious career, when she herself was becoming a celebrity author, Atwood’s pithy comment provides ample evidence of her awareness that her own career trajectory had much in common with Pickford’s. In Margaret Atwood and the Labour of Literary Celebrity, Lorraine York examines Atwood’s acute awareness of the potential and the dangers of celebrity—and the re-wards of managing it wisely. In this lively and provocative book, York focuses on Atwood’s celebrity as the product of Atwood’s early decision to approach the work of writing and publishing as
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".