Achievement Goals and their Underlying Goal Motivation: Does it Matter Why Sport Participants Pursue their Goals?
Bibliographic record
Abstract
This study examined whether the good or bad outcomes associated with mastery-approach (MAP) and performance-approach (PAP) goals depend on the extent to which they are motivated by autonomous or controlled motivation. A sample of 515 undergraduate students who participated in sport completed measures of achievement goals, motivation of achievement goals, perceived goal attainment, sport satisfaction, and both positive and negative affect. Results of moderated regression analyses revealed that the positive relations of both MAP and PAP goals with perceived goal attainment were stronger for athletes pursuing these goals with high level of autonomous goal motivation. Also, the positive relations between PAP goals and both sport satisfaction and positive affect were stronger at high levels of autonomous goal motivation and controlled goal motivation. The shape of all these significant interactions was consistent with tenets of Self-Determination Theory as controlled goal motivation was negatively associated with positive affect and sport satisfaction and positively associated with negative affect. Overall, these findings demonstrated the importance of considering goal motivation in order to better understand the conditions under which achievement goals are associated with better experiential and performance outcomes in the lives of sport participants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".