MétaCan
Menu
Back to cohort

Psychological Imagery in Sport and Performance

2016· reference-entry· en· W2560468593 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

VenueOxford Research Encyclopedia of Psychology · 2016
Typereference-entry
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMental imagePerspective (graphical)Creative visualizationCognitive psychologyPsychologyTask (project management)Motor imageryGuided imageryComputer scienceVisualizationArtificial intelligenceCognitionEngineeringBrain–computer interfaceAnxiety

Abstract

fetched live from OpenAlex

Abstract Imagery, which can be used by anyone, is appealing to performers because it is executed individually and can be performed at anytime and anywhere. The breadth of the application of imagery is far reaching. Briefly, imagery is creating or recreating experiences in one’s mind. From the early theories of imagery (e.g., psychoneuromuscular) to the more recent imagery models (e.g., PETTLEP), understanding the way in which imagery works is essential to furthering our knowledge and developing strong research and intervention programs aimed at enhanced performance. The measurement of imagery ability and frequency provides a way of monitoring the progression of imagery use and imagery ability. Despite the individual differences known to impact imagery use (e.g., type of task, imagery perspective, imagery speed), imagery remains a key psychological skill integral to a performer’s success.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.077
GPT teacher head0.430
Teacher spread0.353 · 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