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Record W2806512709 · doi:10.28968/cftt.v4i1.29629

'Marks on bodies' : agential cuts as felt experience

2018· article· en· W2806512709 on OpenAlexaff
Ardath Whynacht

Bibliographic record

VenueCatalyst Feminism Theory Technoscience · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsMount Allison University
Fundersnot available
KeywordsMetaphorBorderline personality disorderNarrativePsychologyDistressPersonalityVulnerability (computing)Social psychologyPsychoanalysisPsychotherapistArt

Abstract

fetched live from OpenAlex

Reflecting upon the notion of 'marked bodies' (Barad, 2007) as a metaphor for violence, the author draws upon their experience on a long-term, arts-based research/creation project with women who have been diagnosed with borderline personality disorder and critically considers the ways in which 'emotion' can be conceptualized as both a territory and an agential force. Lived experience with borderline personality disorder often involves long and repeated periods of suicidal ideation and self- harm, yet these experiences are often misunderstood and framed in ways that invalidate emotional distress. The author outlines the ways in which vulnerability as method and radical acceptance of emotional contagion (Brennan, 2004) can foster 'differential responsiveness' (Barad, 2007; 2014) to marks on bodies and allow for the emergence of 'borderline narratives' to emerge and intervene within expert knowledge systems.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.026
Scholarly communication0.0090.005
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.298
Teacher spread0.268 · 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 designQualitative
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

Citations8
Published2018
Admission routes1
Has abstractyes

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