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Record W2063947987 · doi:10.1080/10413200.2011.558049

Self-Modeling and Competitive Beam Performance Enhancement Examined Within a Self-Regulation Perspective

2011· article· en· W2063947987 on OpenAlex

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 Applied Sport Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyPerspective (graphical)Psychological interventionSport psychologySelf-efficacyApplied psychologySocial psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The observation of oneself on video that has been edited to show a performance level higher than one can actually perform is a feedforward form of modeling, termed self-modeling (SM; Dowrick, 1999 Dowrick, P. W. 1999. A review of self-modeling and related interventions. Applied and Preventive Psychology, 8: 23–29. [Crossref], [Web of Science ®] , [Google Scholar]). In this research, gymnasts alternated between viewing and not viewing a SM video during their competitive season. Results showed that gymnasts attained significantly higher beam scores when they viewed the video versus when they did not. No differences in self-efficacy were observed using a quantitative measure; however, a qualitative analysis of interviews based on Zimmerman's (2000) Ram, N. and McCullagh, P. 2003. Self-modeling: Influences on psychological responses and physical performance. The Sport Psychologist, 17(2): 220–241. [Crossref] , [Google Scholar] model, indicated that a number of self-regulatory processes, including self-efficacy, were employed.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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