Bibliographic record
Abstract
The following description is provided by the publisher: CONTENTS THE JACOB ZILBER PRIZE FOR SHORT FICTION JUDGE'S ESSAY Casey Plett 6 Pacing THE JACOB ZILBER PRIZE WINNERS Hsien Chong Tan 7 The Last Snow Globe Repairman in the World Cassidy McFadzean 68 Victory Day FICTION Hilary Ball 20 Hibiscus Kate Gorton 27 Centralia Alana Friend Lettner 41 + Gina Hay 48 Palu di Lechi Emily Pegg 58 August on the Lake POETRY Bess Cooley 18 The Other Side 55 The Witch Court Reporter Tasha Hefford 19 Though Not Unpleasantly Amy LeBlanc 24 I know something you don't know Annick MacAskill 25 Ketch Harbour 66 Martinique Beach Natalie De Paz 26 The Universe Is Quietly Disowning Us All Marika Prokosh 37 Inheritance 79 Letter from Voyager 2 Catriona Wright 38 NOTES TOWARDS AN ANTHROPOCENE FABLE AT A RUSSIAN SAUNA IN MISSISSAUGA Jane Shi 40 Loves'eat Manahil Bandukwala 44 What's a bit more blood? Olive Andrews 45 lessons Heather Christle 46 HALF-NOTE Leo Boix 54 A Red Horse Neil Surkan 57 Late Style Dominik Parisien 67 my partner makes of me a poem Natalie Lim 81 Elegy for Opportunity Contributors 83
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.813 | 0.712 |
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".