Training for the future? The relation between future time perspective and sport expertise
Bibliographic record
Abstract
Future time perspective refers to the extent to which individuals consider the future when making decisions about the present (Husman & Shell, 2008). Because development of expertise requires engaging in large amounts of effortful practice (Ericsson et al., 1993) designed to reach goals at a future point in time (Cote, Baker & Abernethy, 2003), we examined whether future time perspective contributes to time spent in sport practice and achieved success. Competitive athletes (n = 363; Mage = 30.7, SD = 12.5; 49% male) from various team and individual sports completed two subscales of Husman and Shell's (2008) Future Time Perspective Scale and reported their weekly structured practice amounts and highest competitive level. An exploratory structural equation model with value (7 items) and connectedness (7 items) subscales had adequate fit: ?2(64) = 143.35, p < .001; RMSEA = .051 [.040-.062]; CFI = .924; TLI = .891. Analyses of variance demonstrated no significant differences between four competitive groups (city/regional, provincial, national, international) for value, F(3, 346) = 0.16, p = .90, or connectedness, F(3, 350) = 0.43, p = .56. No significant correlations were demonstrated between weekly structured practice amount and either value (r = .03) or connectedness (r = -.03). These results suggest that cross-sectional self-report of valuing long-term goals and connecting current actions to future goals do not associate with engaging in more practice, nor to achieving higher levels of sport skill. We consider methodological and conceptual reasons for these findings.Acknowledgments: This work was supported by a Social Sciences and Humanities Research Council of Canada Insight Development Grant 430-2015-00904 (Bradley W. Young, PI).
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".