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Record W3184607756

"A VR Empathy Machine”: Testimony, Recognition, and Affect on Canada Reads 2019

2020· article· en· W3184607756 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian literature · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPoliticsEmpathyCitizenshipTheme (computing)Reading (process)SociologyMemoirLawAestheticsMedia studiesPolitical sciencePsychologySocial psychologyArtComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.179
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it