Bibliographic record
Abstract
Margaret Atwood's relation to biography and autobiography has been the subject of much controversy. Like many writers, she steadfastly resists attempts to read her works as simple reflections of personal experiences; they are, as she constantly reminds her readers, artistic creations that may draw upon but not be reduced to observed experience. Another Canadian writer, Alice Munro, put the case memorably when she observed that writers often use a bit of starter dough from the real world, but the cake that rises from the pan is, of course, another confection altogether. This chapter will not, therefore, consist of any such attempt to read Atwood's works biographically, as fictionalized autobiography. Instead, it will ponder representations of Atwood and her career, and it will use the notion of literary celebrity to do so. There is no doubt that Atwood is the one Canadian writer who can, most unequivocally, be called a literary celebrity, and this chapter will assess not only how she has been represented as such, but how she has intervened as an active, canny agent to shape the discourses surrounding her celebrity. It may, at first glance, seem out of proportion to call any writer a celebrity, given the sort of attention that Hollywood A-list stars attract, but theorists of celebrity see it as a phenomenon that reaches across cultural institutions. As Christine Gledhill writes, the star “crosses disciplinary boundaries.”
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.027 |
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".