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Record W2908384465 · doi:10.1080/14729679.2018.1557060

How Parkour Coaches Learn to Coach: Coaches’ Sources of Learning in an Unregulated Sport

2019· article· en· W2908384465 on OpenAlex
Ethan Greenberg, Diane M. Culver

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Adventure Education & Outdoor Learning · 2019
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoachingPsychologyAthletesPedagogyMedicine

Abstract

fetched live from OpenAlex

Parkour is a relatively new sport, and so there has not yet been much published research relating to parkour, or more specifically, parkour coaching. There is a large body of knowledge relating to how sport coaches learn to coach, but such research has examined regulated sports; that is, sports with national governing bodies. In North America, where this study was conducted, parkour does not have any national governing bodies, rendering it unregulated. When asked how they learned to coach, parkour coaches from this study described the influences of various sources of learning: parkour coaching experience, previous leadership experience, experience as an athlete in parkour and other sports, other parkour coaches, non-parkour coaches, parkour coach education programmes, school, reflection, and the Internet. It will be interesting to see how the specific influences of these sources might change in the future if parkour in North America becomes regulated.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.301
Teacher spread0.282 · 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