Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents.
Bibliographic record
Abstract
This study examined longitudinal trajectories of positive and negative affective states with a sample of 265 adolescent elite hockey players followed across 3 measurement points during the 1st 11 weeks of a season. Latent class growth modeling, incorporating a time-varying covariate and a series of predictors assessed at the onset of the season, was used to chart out distinct longitudinal trajectories of affective states. Results provided evidence for 3 trajectories of positive affect and 3 trajectories of negative affect. Two of these trajectories were deflected by team selection, a seasonal turning point occurring after the 1st measurement point. Furthermore, the trajectories of positive and negative affective states were predicted by theoretically driven predictors assessed at the start of the season (i.e., self-determination, need satisfaction, athletic identity, and school identity). These results contribute to a better understanding of the motivational, social, and identity-related processes associated with the distinct affective trajectories of athletes participating in elite sport during adolescence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".