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Record W2131775966 · doi:10.1123/tsp.14.4.348

More about Exercise Imagery

2000· article· en· W2131775966 on OpenAlexaff
Kimberley L. Gammage, Craig Hall, Wendy M. Rodgers

Bibliographic record

VenueThe Sport Psychologist · 2000
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of AlbertaWestern University
Fundersnot available
KeywordsPsychologyMental imageCognitionPhysical activityPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Imagery plays important cognitive and motivational roles in many areas of life, including sport (Paivio, 1985) and exercise (Hausenblas, Hall, Rodgers, & Munroe, 1999). The purpose of the present paper was to examine how the cognitive and motivational roles of exercise imagery vary with gender, frequency of exercise, and activity type. Participants (n = 577) completed the Exercise Imagery Questionnaire (Hausenblas et al„ 1999) which measures appearance, energy, and technique imagery. Participants, regardless of gender, frequency of exercise, or activity type, used appearance imagery most frequently, followed by technique and energy, respectively. Men used significantly more technique imagery than women did, while women used significantly more appearance imagery than men did. In addition, high frequency exercisers (3 or more times per week) used all types of imagery more frequently than low frequency exercisers (2 or fewer times per week). Finally, imagery differences existed based on type of activity.

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)0.001
metaresearch head score (Gemma)0.004
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: Commentary · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.005
Open science0.0000.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.025
GPT teacher head0.327
Teacher spread0.302 · 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
GenreCommentary

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

Citations83
Published2000
Admission routes1
Has abstractyes

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