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Record W1802460035 · doi:10.14318/hau5.2.012

Descartes’ shadow

2015· article· en· W1802460035 on OpenAlexaboutno aff
Leo Hopkinson

Bibliographic record

VenueHau Journal of Ethnographic Theory · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsDualismEmbodied cognitionReification (Marxism)Shadow (psychology)EpistemologyMind–body problemMistakeConstruct (python library)SociologyAestheticsPsychologyPsychoanalysisPhilosophyPolitics

Abstract

fetched live from OpenAlex

This article explores the body and self engendered through a boxer’s training, drawing on fieldwork conducted in boxing gyms in Montreal and Edinburgh. Contrary to contemporary anthropological accounts of the sport, I argue that training practices in these gyms instill a dualistic sense of self, evocative of Cartesian dualism. Paradoxically this is not alternative to, but concurrent with, a sense of embodied knowledge and selfhood in proficient boxers. Dualistic selfhood is traced throughout training regimes and in a boxer’s progress from novice to experienced pugilist, considering the different practices developed and encountered during this progress. I conclude by problematizing the anthropological fear of the Cartesian body. By treating the Cartesian body as a philosophical mistake rather than a social reification, social scientists working with concepts of body and self risk creating a straw man that inhibits their capacity to analyze mind-body dualism as a social construct.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.011
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0230.008

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 head0.073
GPT teacher head0.334
Teacher spread0.261 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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