Leveraging participation in Olympic sports: a call for experiential qualitative case study research
Bibliographic record
Abstract
Through this research note, we intend to advocate the importance of investigating introductory sport programmes in connection with leveraging initiatives to understand: (1) the processes of, as well as the forces shaping, the development of introductory sport programmes, (2) the experiences of participants in these programmes and (3) how these programmes connect to broader leveraging structures and initiatives. Additionally, a qualitative case study methodology presents an effective research strategy to achieve these goals. We also outline the promise of a qualitative case study methodology by illustrating its potential for deepening our understanding of leveraging-related programme development. While past research has focused on the construction of these programmes, we argue for the need to explore what the leveraging experience is like for those engaging in introductory sport programmes. Through exploring the development and experiences of introductory sport programmes, scholars can develop new research directions and questions, as well as inform future leveraging initiatives.
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 | 0.142 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".