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Record W2765699207 · doi:10.1177/1747954117739717

The learning pathways of Brazilian surf coach developers

2017· article· en· W2765699207 on OpenAlexaff
Vinícius Zeilmann Brasil, Valmor Ramos, Michel Milistetd, Diane M. Culver, Juarez Vieira do Nascimento

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2017
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSocializationThematic analysisContext (archaeology)SituatedPsychologyFormative assessmentPerceptionApplied psychologyQualitative researchSocial psychologySociologyPedagogyComputer scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the learning pathways of five Brazilian surf coach developers, in order to understand how they became coach developers. A case study was conducted with five surf coach developers working in the sport participation context, and linked to a legally organized Brazilian surf federation. Three main research topics guided the semi-structured interviews: participants’ experiences as a surfer, as a surf coach, and as a coach developer. Data were analyzed using thematic analysis to explore the participants’ perceptions of the experiences around becoming a surf coach developer. The study revealed a pattern of formative experiences for the participants, across their lives and careers. Their experiences as a surfer and as a surf coach, as well as their exposure to the surfing environment and their contact with significant others, influenced in their engagement in surfing and in the surf coach context; leading them eventually to the desire to share knowledge with others. Becoming a surf coach developer in this study corresponded to a mutual socialization process across a lifetime. This process was marked by situated socio-cultural aspects of different life phases, strongly influenced by the social relations established in immediate contexts (family) and with other specific groups (surfers, coaches, and developers).

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)0.004
metaresearch head score (Gemma)0.001
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 score0.333
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.351
Teacher spread0.317 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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