A qualitative analysis of goal construal level in sport performance
Bibliographic record
Abstract
Effects of goal-setting on performance have been well-examined in organizational settings, though questions remain unanswered regarding the effectiveness of goal-setting in a sport context, and underlying factors that explain ambiguities in obtained results (Locke & Latham, 2002; Latham & Seijts, 2001). One prominent criticism of goal-to-performance research is an "omssion in studying naturally set goals", as the goal content is often supplied by the researcher (Kane et al., 2001). We conducted phenomenological, semi-structured interviews with 12 varsity athletes from various sports (martial arts, table tennis, track-and-field sports) to add to the body of goal-setting research by providing information about the cognitive and imagery processes behind athletes' naturally set goals. Led by concepts based on Construal Level Theory (CLT; Liberman et al., 1998), we found a thematic clustering by task demands (dynamic vs stable task context, e.g. table tennis point vs long jump), as various tasks require a processing of either more proximal or distant stimuli. In line with previous research in CLT, these functional cues appear to shape evaluations on different levels of abstraction (i.e., goals on either a low or high construal level). Depending on their task context and individual thinking styles, as measured by the Behavior Identification Form (Vallacher & Wegner, 1989), athletes also perceived either low or high construal as more motivating and helpful. Furthermore, the time distance dimension shapes goal construal as CLT would suggest, with athletes creating mental representations on different levels of abstraction, dependent on and influenced by the distance relation to their goal.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".