REWARDING CIVILITY IN CANADA’S BATTLE OF THE BOOKS: CANADA READS AND THE POLITE DISCOURSE OF ELIMINATION
Bibliographic record
Abstract
This thesis looks at three seasons of the Canadian Broadcasting Corporation’s (CBC) radio show Canada Reads – 2014, 2015, and 2016. I examine how each year’s debates over reconciliation (2014), inclusive multiculturalism (2015), and Canada’s role as a global refuge (2016) commonly presume a national mythology that Indigenous peoples have either disappeared or become “Canadian.” I argue that despite the show’s desire to build a better society through encouraging Canadians to read Canadian books, the debates featured on Canada Reads reflect the way assumed Canadian control of Indigenous lands is embedded in the language of Canadian literature and culture to both limit the political disruptiveness of Indigenous presence and reproduce ongoing colonial domination. Central to my argument is the sad truth that, even as the show invites diverse critiques of Canadian society, it nonetheless favours stereotypical narratives of Canadian multiculturalism, benevolence, and civility, and by doing so buttresses Canada’s unchanged status as a settler colonial state. I track and evaluate ruptures in the show's civil language and decorum by reading moments of debate when the logical foundations of these stereotypical national narratives are challenged. Thus, this thesis examines not only what panelists say to each other, but also what their dialogue says to other Canadians. I argue that panelists’ critiques of the nation drawn from their readings of the books - readings that are not so much holistic interpretations of books but strategies for winning the Survivor-style game - are welcomed by the show’s annual social justice themes which then use them to purvey the nation’s virtuous liberalism. Ultimately, my analysis traces how the civil protocols of the program through these three seasons reproduce conflicts between Indigenous peoples and Canadians by reinforcing the inequity of the arrangements of the existing nation-state.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.059 | 0.038 |
| Scholarly communication | 0.023 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".