"A VR Empathy Machine”: Testimony, Recognition, and Affect on Canada Reads 2019
Bibliographic record
Abstract
Guided by the “one book to move you” theme, Canada Reads 2019 enacts a vernacular mode of shared reading that relies on affective-driven responses framed as the cure for rising socio-political maladies. Given the mix of fiction and memoirs in the final roster, I address the truth-value invoked in the debates through the prism of testimony, and readers’ ethical responsibility to its rights-claims. Building on the works of Danielle Fuller and DeNel Rehberg Sedo, Pauline Wakeham, Gillian Whitlock, and Carolyn Pedwell, I demonstrate how the 2019 event, as a site of reading-based public debate, contours the limits of empathy as an ethical response to testimony. I argue that the political efficacies of empathy map the cunning discourse of political recognition onto the politics of reading in Canada Reads 2019—presumably effecting socio-political change while de-facto mobilizing literature in service of the humanitarian and multicultural myths of CanLit readership and citizenship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.019 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".