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Record W4200221552 · doi:10.25071/2564-4661.21

"How do you Criticize a Life Story?"

2021· article· en· W4200221552 on OpenAlexaffabout
Taylor Brown

Bibliographic record

VenueContemporary Kanata Interdisciplinary Approaches To Canadian Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMemoirReading (process)LiteratureCONTESTPopularityArtHistoryAestheticsSociologyPsychologyPhilosophyLawLinguisticsPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

“‘How do you Criticize a Life Story?’: Form, Trauma, and Memoir in Canada Reads 2020” investigates the practice of reading for empathy, as it pertains to memoir and trauma operating in the hypervisibility of the public sphere. The emotional connection between reader and author that memoir inspires is also encouraged on Canada Reads, the popular intersection of a literary contest and reality show. The panelists’ 2020 discussion of Jesse Thistle’s From the Ashes and Samra Habib’s We Have Always Been Here encouraged reading as a means of empathizing with the author’s experiences. As Danielle Fuller details, this is also how many viewers appraise the titles featured on Canada Reads, adopting a method of literary evaluation that is inherently personal. Memoir, given its connection to the real world and real people, becomes an excellent candidate for connecting with the reader. While Philippe Lejeune argues that memoir must be entirely non-fictional, G. Thomas Couser and Leigh Gilmore demonstrate that for a genre grappling with selective memory and trauma, this is impossible. As a result, memoir proves to be a genre that is both popular amongst readers and necessarily literary and inventive in its construction. The popularity of Canada Reads and memoir indicate that empathetic reading deserves a place in literary discourse, which in turn reimagines the Canadian literary canon and traditional methods of evaluation.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.204
GPT teacher head0.312
Teacher spread0.108 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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