Challenges and opportunities arising from self-regulated professionalisation processes: an analysis of a Swiss national sport federation
Bibliographic record
Abstract
In recent decades, some governments (e.g. Canada, the UK, Australia) have imposed far-reaching professionalisation processes on national sport federations (NSFs), while others (e.g. Switzerland) have made only minor impositions and relied more on NSFs to self-regulate. As governments must decide on the extent to which sport policy imposes professionalisation processes on NSFs, understanding the challenges and opportunities arising from both policy-imposed and self-regulated professionalisation processes is relevant. However, extant literature has focused mostly on professionalisation processes imposed by sport policy. Therefore, this study aims to analyse the context, action, content and outcome of self-regulated professionalisation processes to identify the challenges and opportunities arising from these processes. A framework of professionalisation and a corresponding processual approach build the conceptual background of this study. A single-case study is applied to enable a holistic and long-term analysis of the proceedings of a Swiss NSF’s professionalisation processes. The results reveal the mechanisms of self-regulated professionalisation processes (i.e. how contexts and actions shape outcome), thus leading to a conceptualisation of these mechanisms and conclusions about challenges and opportunities arising from self-regulated professionalisation processes, which are useful for sport managers and policymakers.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".