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Record W4388223486 · doi:10.4324/9781003365082-13

‘A Skin of One’s Own'

2023· book-chapter· en· W4388223486 on OpenAlexaboutno aff
Arya Thampuran

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDermatologyArtMedicine

Abstract

fetched live from OpenAlex

This chapter interrogates the Euro-centric explanatory frameworks used to order and interpret embodied expressions of trauma. It places in dialogue two texts that demand a decolonial practice of attending to expressions of trauma. This involves an ethics of reading the body – specifically, skin-based practices – as traumatic testimony in ‘post-colonial’ texts, one that resists a singular Western frame of selfhood, and the attendant linear wellness narrative couched in a discourse of pathology and cure. It first considers autobiographical accounts of Canadian skin grafting on Inuit children, part of a vexed history of colonial medical experimentation from the 1950s–70s. It further explores the re-narrativisation of traumatic testimony through Igbo-Tamil writer Akwaeke Emezi’s semi-autobiographical literary debut, Freshwater , where the protagonist’s experiences of hearing voices and self-mutilation complicate a DSM-based psychiatric reading, framed as the text is through the Igbo ontology of the malevolent born-to-die ogbanje child. Drawing from these accounts, the chapter interrogates both the corporeal and epistemic violence enacted within a (neo)colonial circulation of biopower. Ultimately, it illuminates the agentive potential in these narrative acts that address and redress such violence. Keywords: Decolonial; intersectionality; medical humanities; race; African literature; mental health; psychiatry, mythology; body studies

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)0.003
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: Other
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.033
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.070
GPT teacher head0.211
Teacher spread0.142 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same topicPostcolonial and Cultural Literary StudiesFrench-language works237,207