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Record W3040171472 · doi:10.3138/utq.89.1.06

Feeling Sideways: Shani Mootoo and Kai Cheng Thom’s Sustainable Affects

2020· article· en· W3040171472 on OpenAlexvenueno aff
Libe García Zarranz

Bibliographic record

VenueUniversity of Toronto Quarterly · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingQueerSociologyShameAffect (linguistics)AestheticsGender studiesStorytellingNormativePsychoanalysisPower (physics)EmpathyPsychologyNarrativeSocial psychologyEpistemologyArtLiteraturePhilosophyCommunication

Abstract

fetched live from OpenAlex

Drawing on feminist, queer, trans, and anti-racist theory, this article examines the transmission of affect between racialized queer and trans subjects as a mode of what I call feeling sideways. With this formulation, I seek to spark a discussion about the need to dislodge affect not only from gender and racial normative systems of power but also from linear understandings of growth and temporality. I here follow Kathryn Bond Stockton’s theories of sideways growth (2009) and Heather Love’s notion of backward feelings (2009) to unravel modes of feeling sideways as a potential form of what I call sustainable affect. As case studies, I put two transCanadian works beside each other: Shani Mootoo’s novel Moving Forward Sideways like a Crab (2014) and Kai Cheng Thom’s poetry collection a place called No Homeland (2017). In different but related ways, these texts illustrate how shame, anger, and empathy can become sustainable affects through touch and storytelling, with important ethical repercussions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.017
Scholarly communication0.0040.004
Open science0.0000.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2020
Admission routes1
Has abstractyes

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