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Record W4281291077 · doi:10.3389/frym.2022.686407

All Athletes Can Lead in Their Own Way

2022· article· en· W4281291077 on OpenAlex
Todd M. Loughead, Mason B. Sheppard, Katherine E. Hirsch

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

VenueFrontiers for Young Minds · 2022
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAthletesPsychologyShared leadershipPublic relationsPolitical scienceLeadership styleSocial psychologyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Did you know that not just coaches can be leaders on sport teams? Athletes are also an important source of leadership within teams. When you think of athletes performing leadership roles, you probably think of captains or assistant captains. While these are important sources of team leadership, athletes do not need to be captains or assistant captains to be leaders. In fact, all athletes can display leadership through their behaviors. Coaches can help athletes to be a part of team leadership. We provide some suggestions on how coaches can facilitate the development of leadership skills in their athletes. If athletes are not comfortable being team leaders, they can provide leadership by mentoring fellow teammates. There are many ways that athletes can provide leadership to their teams!

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.269
Teacher spread0.245 · 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