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Record W3087169438 · doi:10.25071/2563-3694.14

Mad Insight

2020· article· en· W3087169438 on OpenAlexaffvenue
Renee Dumaresque

Bibliographic record

VenueNew Sociology Journal of Critical Praxis · 2020
Typearticle
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsYork University
Fundersnot available
KeywordsHysteriaPsychicPsychologyMetaphorPsychoanalysisPoetryReading (process)Resistance (ecology)LiteratureArtPhilosophyMedicineLawPolitical science

Abstract

fetched live from OpenAlex

This poem is informed by the relationships between gender, race, chronic pain, hysteria, and the role of dominant discourses in shaping interpretations of bodily and psychic pain. Drawing on my lived experience as a non-binary person with chronic vulvar pain, or vulvodynia, I challenge the psychiatrization of chronic pain and propose hysteria as a potential state of resistance and refusal (Dumaresque, 2019). I weave fog throughout this poem as a metaphor that captures pain, madness, and perception. Fog symbolizes disruption and disorientation; yet, fog also gestures to the potentiality of being displaced from normative insight (Bruce, 2017). I engage William Connolly’s (2010) reading of perception as formed through discipline to think through the silent but subversive waves of knowledge and power that carve the lenses through which we story ourselves and others (Erickson, 2016). As Thomas King (2003) writes, “the truth about stories is that’s all we are” (p. 32). This poem is situated in a reading of madness and hysteria as sites of affective protest (Dumaresque, 2019). I ask, what can be resourced from our becoming un-hinged? This poem contributes to mad knowledge that is intersectional and in-service to disrupting medical and psychiatric violence, whiteness, hetero/cis-governance, and “compulsory able-bodymindedness” (Sheppard, 2018, p. 59).

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)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.066
GPT teacher head0.400
Teacher spread0.334 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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