Bibliographic record
Abstract
Producing Canadian Literature: Authors Speak on the Literary Marketplace brings to light the relationship between writers in Canada and the marketplace within which their work circulates. Through a series of conversations with both established and younger writers from across the country, Kit Dobson and Smaro Kamboureli investigate how writers perceive their relationship to the cultural economy—and what that economy means for their creative processes. The interviews in Producing Canadian Literature focus, in particular, on how writers interact with the cultural institutions and bodies that surround them. Conversations pursue the impacts of arts funding on writers; show how agents, editors, and publishers affect writers’ works; examine the process of actually selling a book, both in Canada and abroad; and contemplate what literary awards mean to writers. Dialogues with Christian Bök, George Elliott Clarke, Daniel Heath Justice, Larissa Lai, Stephen Henighan, Roy Miki, Erín Moure, Ashok Mathur, Lee Maracle, Jane Urquhart, and Aritha van Herk testify to the broad range of experience that writers in Canada have when it comes to the conditions in which their work is produced. Original in its desire to directly explore the specific circumstances in which writers work—and how those conditions affect their writing itself— Producing Canadian Literature will be of interest to scholars, students, aspiring writers, and readers who have followed these authors and want to know more about how their books come into being.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.015 |
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".