Bibliographic record
Abstract
The CLC Kreisel Lecture Series is an annual event dedicated to nurturing both public and scholarly engagement with the critical concerns of writers in Canada.Each year, an established author is invited to speak about an issue that is important to them, whether because it is close to their heart, foundational to their practice, or a pressing cultural concern; often, it is all these things at once.The series showcases the myriad ways in which writers help us understand the textures of life in this country: it includes lectures about oppression and social justice, cultural identity, place and displacement, the spoils of history, censorship, multilingualism, reading in a digital age, literary history, personal memory, Indigenous resurgence, and the essential function of art.Usually delivered to a live audience on the University of Alberta campus on Treaty 6 Territory and Region 4 of the Métis Nation of Alberta, the Kreisel Lectures frequently also air to audiences across Canada as episodes on CBC Radio's Ideas.All of our lectures become books like this one, published in partnership with University of Alberta Press.Our 2022 lecturer, Cherie Dimaline, is the acclaimed Georgian Bay Métis author of the award-winning YA novel The Marrow Thieves (2017), Empire of Wild (2019), and Hunting by Stars (2021), among other works.As Anna Marie Sewell, amiskwaciwâskahikan / Edmonton writer of Polish and Mi'kmaq descent, noted in her introduction to the lecture, Dimaline is a powerful storyteller who recognizes how important it is for Indigenous youth to see themselves in "stories that matter."Dimaline's work is far reaching, her stories critical for readers of all ages-Indigenous and settler alike.Her literary oeuvre brings together themes of resistance to settler colonialism (including the legacies
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.505 | 0.481 |
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".