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Record W2100640451 · doi:10.1080/01596301003679727

Coming to know oneself through experiential education

2010· article· en· W2100640451 on OpenAlexaff
Robyn Zink

Bibliographic record

VenueDiscourse Studies in the Cultural Politics of Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNothingExperiential learningPleasureMaximPsychologySchema (genetic algorithms)SelfEmbarrassmentEpistemologySociologyAestheticsSocial psychologyPedagogyPhilosophyComputer science

Abstract

fetched live from OpenAlex

This article draws on the work of Foucault to explore why students on a residential program talk about learning about themselves as if it were an epiphany and one of the most empowering aspects of the program. Foucault's schema of turning to the self suggests that the pleasure students experience at ‘discovering’ themselves is a logical response to what he terms as one of the most powerful technologies of the self. Butler's work on giving an account of oneself is used to investigate the terms through which learning about the self occurs. She extends and inverts Foucault's schema, suggesting that one is only required to give an account of the self in the face of another. To become self-knowing requires recognition by another and recognition of others. While contemporary experiential education has been shaped by the maxim that nothing is more relevant to us than ourselves, I argue that perhaps this maxim should read; ‘Nothing is more relevant to us than those around us’.

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)0.012
metaresearch head score (Gemma)0.013
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 score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.037
Scholarly communication0.0100.014
Open science0.0020.016
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.063
GPT teacher head0.487
Teacher spread0.425 · 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

Citations27
Published2010
Admission routes1
Has abstractyes

Explore more

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