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Record W1505897404 · doi:10.1123/ssj.24.1.78

Seeing Your Sporting Body: Identity, Subjectivity, and Misrecognition

2007· article· en· W1505897404 on OpenAlexaff
Michelle T. Helstein

Bibliographic record

VenueSociology of Sport Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSubjectivityPsychoanalytic theoryIdentity (music)PsychicIdentification (biology)EpistemologyArticulation (sociology)SociologyAestheticsAnalogyPsychoanalysisSubjectificationSelfReading (process)PoliticsConversationPsychologyPhilosophyLinguisticsLawCommunication

Abstract

fetched live from OpenAlex

This article draws on the work of two poststructural theorists, Michel Foucault and Jacques Lacan, to illustrate that although it is possible to posit identity from an exclusively discursive account (Foucault) or an exclusively psychoanalytic account (Lacan), it is necessary to put such accounts into conversation to more productively engage in the process of identification. Through use of an advertisement (in which a female athlete sees herself in a mirror) and an analogy to the scientific laws of reflection, this article illustrates that in order to see oneself (identify) one must recognize something in, on, or through their body, and this recognition of the body is always a misrecognition that might more appropriately be called identification. This article is therefore a reading of identification through the productive exploration of the woman on both sides of the mirror, highlighting both discursive and psychoanalytic accounts of her subjectivity. The pervasiveness of the body within these accounts is notable because it highlights the possibilities of the body as a point of articulation between discursive and psychic accounts of identification. The article also illustrates that even when identity is acknowledge as constructed, fragmented, and multiple, it is still meaningful, material, and political.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.359
Teacher spread0.313 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2007
Admission routes1
Has abstractyes

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