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Record W2513450900 · doi:10.1177/0309132516660207

From post-game to play-by-play

2016· article· en· W2513450900 on OpenAlexaff
Gavin J. Andrews

Bibliographic record

VenueProgress in Human Geography · 2016
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVitalityScholarshipAestheticsFeelingField (mathematics)SociologySpace (punctuation)PoliticsEpistemologySocial psychologyPsychologyPolitical scienceLawComputer scienceArt

Abstract

fetched live from OpenAlex

As a field of research or possible sub-discipline, sports geography has not realized its full potential. This paper summarizes some of the main subjects investigated and approaches taken to date, then using this as a launching point, describes a particular way forward for research. It is argued that a better engagement with, and showing of, the physicality, energy and feeling of sport might be achieved through employing non-representational theory, itself involving an emphasis on exposing the immediate and moving in life, including the less-than-fully conscious practices, performances and sensations involved. In particular, these arguments are framed by discussions of some of the fundamental qualities of ‘movement-space’ that might be more clearly animated in future scholarship – specifically rhythm, momentum, vitality, infectiousness, imminence and encounter – and are supported by highlighting some pathbreaking sports geographies that have already begun to convey them. It is argued that, whilst these qualities are critical to sport in their own right, importantly they interplay with social, political and economic processes in sport that geographers already have a modest record of engaging with.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0110.007
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0350.011

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 head0.014
GPT teacher head0.312
Teacher spread0.298 · 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 designNot applicable
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

Citations42
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProgress in Human GeographySame topicAdventure Sports and Sensation SeekingFrench-language works237,207