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Record W1950182085 · doi:10.29173/pandpr25361

Riding in the Skin of the Movement: An Agogic Practice

2015· article· en· W1950182085 on OpenAlexaffvenue
Stephen J. Smith

Bibliographic record

VenuePhenomenology & Practice · 2015
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsBurnaby HospitalSimon Fraser University
Fundersnot available
KeywordsContemplationMovement (music)Resistance (ecology)PsychologyAestheticsDanceRelation (database)EpistemologyCommunicationSocial psychologyPhilosophyComputer scienceVisual artsArt

Abstract

fetched live from OpenAlex

The art of riding imagines the human-horse relation in the image of the centaur. In synchronous motions, riding is a dance of sorts, contact of bodies in the skin of the moment. Yet always there is the possibility of fussing, flailing, falling and failing in moments of resistance, evasion and contrariness. Through phenomenological reflection on such moments, riding can be understood not simply in terms of its difficulties of centaurian mastery, but in terms of the postural, positional, gestural, expressive nuances of interspecies communication. It is on the off beats, and within the syncopations and momentary stresses of riding, that resistance can be addressed through quiet insistence, evasions overcome through persuasion, and contrariness can be felt otherwise. Through contemplation of such moments, we find the reminders of a sensual and essential intercorporeality and the configuration of an agogic practice.

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)0.005
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: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.032
Scholarly communication0.0050.004
Open science0.0010.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.096
GPT teacher head0.380
Teacher spread0.284 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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