“We get to the places we get to the way we do:” An Interview with Suzette Mayr
Bibliographic record
Abstract
Suzette Mayr is the author of five novels including her most recent book, Dr. Edith Vane and the Hares of Crawley Hall. Her fourth novel, Monoceros, won the ReLit Award and the City of Calgary W.O. Mitchell Book Prize, was longlisted for the 2011 Giller Prize, and nominated for a Ferro-Grumley Award for LGBT Fiction and the Georges Bugnet Award for Fiction. Monoceros was also included on The Globe and Mail’s 100 Best Books of 2011. Her novels have also been nominated for the regional Commonwealth Writers’ Prize and the Henry Kreisel Award for Best First Book. She has done inter-disciplinary work with Calgary theatre company Theatre Junction and visual artists Lisa Brawn and Geoff Hunter. She was also a writer-in-residence at the University of Calgary and at Widener University, Pennsylvania. She is a former president of the Writers’ Guild of Alberta and teaches Creative Writing at the University of Calgary. Among other things, this interview touches on Mayr’s interest in the supernatural; what it means to be a prairie writer; her most recent novel, Dr. Edith Vane and the Hares of Crawley Hall; the tensions between academia and art; and writing during a global pandemic.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".