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Subject/ive bodies: the resistance poetics of Chrystos and Mahadai Das

2021· article· en· W3172774284 on OpenAlexaff
Geoffrey MacDonald

Bibliographic record

VenueRadical Americas · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsSociologyGender studiesIdeologyPoeticsResistance (ecology)AestheticsSubjectivityColonialismIndigenousHegemonyPoliticsPoetryLiteratureEpistemologyPolitical scienceLawArtPhilosophy

Abstract

fetched live from OpenAlex

Recent criticism has considered how literary texts harness historical and ideological forces in the representation of the body. However, much of that scholarship focuses on hegemonic structures such as Western medicine, post-human technologies or colonial race theories. This article looks at how two poets from the Americas – Indigenous North American Chrystos (Menominee) and Mahadai Das from Guyana – express representations of the body from a position of marginalisation to emphasise the connections between individual subjectivity and social transformation. I discuss the body as theme for producing a resistance poetry that directly connects desire, disaffection, sexuality and mourning to decolonisation. I perform close readings that emphasise the linkages between intimate relations and social movements. Chrystos and Das speak to a constitutive divide in post-colonial studies between the personal and political in what is called resistance literature. By centring deeply personal perspectives on decolonial struggle within a figurative context that encourages contemplation and complexity, these poets contribute to a diversification of resistance theory that addresses gender, anti-racist, sexual diversity and other movements of the last few decades.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.026
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.003
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 head0.020
GPT teacher head0.309
Teacher spread0.288 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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