“True Stories,” Real Lives: Canada Reads 2012 and the Effects of Reading Memoir in Public
Bibliographic record
Abstract
For the 2012 instalment of the competitive reading radio show Canada Reads, the producers decided to feature what they called “True Stories,” with the winner declared as the nonfictional work all Canadians should read. This was the first year of Canada Reads to feature a theme, and the first to focus on nonfiction. However, the producers’ decision to switch from fiction genres to nonfiction genres had several unforeseen effects within the show and the public realm, including a controversy generated by panellist Ann France Goldwater when she called author Carmen Aguirre a terrorist, and accused author Marina Nemat of falsifying details in her memoir Prisoner of Tehran. In this essay we propose that the prominence of the memoir genre on Canada Reads 2012 created a series of effects on the show and in public which disrupted the usual “show business” of the program as public entertainment and economic catalyst, helping to create a controversy that spilled over into public discourse. The effects of reading memoir were very different from the effects of reading fiction on the show. Memoir’s effects as a genre helped to change the character of Canada Reads itself from an amusing game show about the implicit power and goodness of reading, to a serious debate about Canadian identity and citizenship.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.024 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".