Bibliographic record
Abstract
EDITORIAL EDITORIAL Women and Texts: Part One This is the first of two special issues of English Studies in Canada on “Women and Texts.” The second special issue on this subject will be the June 2002 number of the journal. The writers whose articles appear in these two issues of ESC were among those who submitted papers in response to our call for “critical and scholarly articles” on “such subjects as the history of women’s writing, its publication and critical reception, its theorization, its con tribution to politics and culture, and the role that women have played as editors and publishers as well as creators of literature in English.” The responses we received to this call may not have covered quite all these topics, but they certainly ranged over the fields of literature in English and used diverse critical approaches. In putting together “Women and Texts: Part One,” we aimed to show this variety. We also wished to highlight women not only as composers of articles for learned journals, but also as writers and reviewers of volumes of criticism and scholarship. That neither women nor men work in isolation is signaled in both parts by the men who have reviewed books by women and by the women who have reviewed books by men. May the readers of ESC enjoy both this issue and “Women and Texts: Part Two” in our 2002 volume. Mary Jane Edwards Editor, ESC 251 ...
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".