Development and initial factor validation of the French Conformity to the Sport Ethic Scale (CSES).
Bibliographic record
Abstract
Coker-Cranney et al. (2018) recently stressed the need for the development and validation of a questionnaire that assesses a young athlete’s level of conformity to sport ethic norms. The objective of this study was to develop and begin an initial factor validation of the Conformity to the Sport Ethic Scale (CSES), a scale assessing the conformity of teenage athletes from all competition levels to these sport ethic norms. Following the steps suggested by DeVellis (2012) for scale development and validation, a convenience sample of 1096 French-Canadian athletes between 14 and 18 years and who participated in an organized sport were recruited to partake in an online study assessing their conformity to the sport ethic. The CSES was developed to include four dimensions based on the qualitative work of Hughes and Coakley (1991), namely, self-sacrifice, striving for distinction, accepting risks/playing through pain and refusing to accept limits. To identify latent factors underlying the CSES exploratory structural equation modeling (ESEM) was performed using Mplus version 8.0. The CSES includes 20 items in three factors: striving for distinction (6 items), self-sacrifice (4 items) and refusing to accept limits (10 items). The resulting factor structure was invariant between boys and girls. Internal consistency of these subscales was acceptable. All correlations between subscales were significant. This tool is the first step to measure conformity to the Sport Ethic norms and will allow for further research in this area.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".