Bibliographic record
Abstract
Elite sport currently enjoys high levels of investment in many advanced capitalist countries. The primary aim of this piece is to introduce and unpack the reasons generally given by states for prioritizing and investing in elite sport. While our core focus is the UK sport policy sector, many of the discussions will be relevant for other, advanced liberal capitalist systems (e.g. Australia and Canada) and even the now defunct dictatorships (e.g. the Soviet Union and the GDR). We show how commonsensical propositions (e.g. ‘elite sport success promotes participation among citizens’) are not always based on wide, existing research and evidence. The philosophy behind the United Kingdom's model of sport – and that of several other advanced states – we term a ‘virtuous cycle’ of sport, whereby elite sport success is seen to lead to both international prestige for the nation, a ‘feel-good factor’ among the population and, importantly, to an increase in participation among the masses. This, in turn, leads to a healthier nation and to a wider pool of people from which to pick the champions of the future. This article takes a closer look at the assumptions underlying such a model of sport.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".