BECKS AND POSH AND ALL THAT: REFLECTIONS ON SPORTING CELEBRITY
Bibliographic record
Abstract
Two recent books dealing with sports stars and what being famous as a sportsperson involves raise questions about celebrity and stardom in the sporting world. They are David Andrews ' and Steven Jackson's Sport Stars: the cultural politics of sporting celebrity (2001); and a whole book by one of the contributors to Andrews & Jackson, Garry Whannel, called Media Sports stars: masculinities and moralities, published in 2002. Both books come from Routledge, and it is hard to say whether we are in for lots of this research or whether it is Routledge having got interested in celebrity. Some of the matters they raise offer ideas to sports historians for research topics. Andrews and Jackson are non-historical, dealing with prominent sportspersons of today, whereas Whannel sets his celebrities within a historical framework, though not in a detailed way. Andrews and Jackson have chapters on athletes American and Canadian, Australian and Kenyan, British and West Indian. Jackson and another author are from New Zealand. The book is designed, as you will
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".