Bibliographic record
Abstract
Can a case be made for reading literature in the digital age? Does literature still matter in this era of instant information? Is it even possible to advocate for serious, sustained reading with all manner of social media distracting us, fragmenting our concentration, and demanding short, rapid communication? In The Edge of the Precipice, Paul Socken brings together a thoughtful group of writers, editors, philosophers, librarians, archivists, and literary critics from Canada, the US, France, England, South Africa, and Australia to contemplate the state of literature in the twenty-first century. Including essays by outstanding contributors such as Alberto Manguel, Mark Kingwell, Lori Saint-Martin, Sven Birkerts, Katia Grubisic, Drew Nelles, and J. Hillis Miller, this collection presents a range of perspectives about the importance of reading literature today. The Edge of the Precipice is a passionate, articulate, and entertaining collection that reflects on the role of literature in our society and asks if it is now under siege. Contributors include Michael Austin (Newman University), Sven Birkerts (author), Stephen Brockmann (Carnegie-Mellon University), Vincent Giroud (University of Franche-Comté), Katia Grubisic (poet), Mark Kingwell (University of Toronto), Alberto Manguel (author), J. Hillis Miller (University of California, Irvine), Drew Nelles (editor-in-chief, Maisonneuve), Keith Oatley (University of Toronto), Ekaterina Rogatchevskaia (British Library), Leonard Rosmarin (Brock University), Lori Saint-Martin (translator, Université du Québec à Montréal), Paul Socken (University of Waterloo), and Gerhard van der Linde (University of South Africa).
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 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".