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Record W3177728754 · doi:10.1123/jsep.2021-0048

The Two Dimensions of Passion for Sport: A New Look Using a Quadripartite Approach

2021· article· en· W3177728754 on OpenAlexaff
Benjamin J. I. Schellenberg, Jérémie Verner‐Filion, Patrick Gaudreau, Sophia Mbabaali

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of OttawaUniversité du Québec en OutaouaisUniversity of Manitoba
Fundersnot available
KeywordsPassionAthletesPsychologySocial psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Research relying on the dualistic model of passion has consistently found that harmonious passion for sport is positively associated with adaptive outcomes and that obsessive passion for sport is positively associated with maladaptive outcomes. In this research, we tested if various sport outcomes were related to within-person combinations of both harmonious and obsessive passion. Three samples of athletes (total N = 1,290) completed online surveys that assessed various sport outcomes (e.g., sport enjoyment, goal attainment), along with harmonious and obsessive passion for their sport. We found that athletes were best served by having either high harmonious passion or low obsessive passion or, in many cases, high harmonious passion that was combined with low obsessive passion. These results add to our understanding of passion by showing that combinations of harmonious and obsessive passion for sport are differentially associated with indicators of a positive sport experience.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.047
GPT teacher head0.353
Teacher spread0.307 · 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 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

Citations22
Published2021
Admission routes1
Has abstractyes

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