Bibliographic record
Abstract
The purposes of this study were to (a) examine coaches’ perspectives on contribution through sport and (b) obtain their feedback on a previously established definition of contribution. Data were collected via focus groups with 13 coaches from a variety of individual and team sports (M age = 33 years, SD = 11.1). Focus group transcripts were analysed using reflexive thematic analysis [Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806]. Findings were presented as two categories pertaining to the two purposes of the study. In the first category, youth sport coaches’ perceptions of contribution, the coaches’ discussion of contribution centred on three themes. Coaches discussed contributing as an athlete by providing sport specific examples of contribution behaviour. Through all their discussions, it was evident that coaches perceived that contribution involves having a positive impact and acting with intent. Regarding the second category, coaches’ feedback on the definition, coaches expressed that the definition fit with their conceptualisations of contribution, particularly the first sentence which contained the ideas that contribution is intentional and results in positive impacts on others. However, the coaches felt the definition was overly complex and questioned whether the definition should have focused on intent versus behaviour. A practical operational definition of contribution was suggested to address the coaches’ criticisms of the theoretical definition. These findings suggest that, whereas the theoretical definition of contribution is appropriate for academic discourse, the practical operational definition may be better suited for use by stakeholders and as a basis for interactions between researchers and sport stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".