Representing the Sporting Past in Museums and Halls of Fame
Bibliographic record
Abstract
Foreword Kevin Moore. Introduction: Historians in Sport Museums Murray G. Phillips Making Meaning 1. A Racehorse in the Museum: Phar Lap and the New Museology Mark O'Neill and Gary Osmond 2. Beyond Sport Heroes' Celebration: On the Use of Sportswear for Sport History Thierry Terret 3. Not So Much a Sport Museum: The Whyte Museum of the Canadian Rockies Douglas A. Brown Corporate Museums 4. Croke Park: Museum, Stadium and Shrine for the Nation Mike Cronin 5. Le Musee Olympique: Epicentre of Olympic Evangelism Daryl Adair 6. Renamed, Refurbished and Reconstructionist: Comparisons and Contrasts in Four London Sports Museums Wray Vamplew Post-Museums 7. The Hall, Wall and Page of Fame Colin Tatz 8. Looking for the 'Marvellous' in Baltimore: A Sport History Sojourn Daniel A. Nathan 9. Bondi Park: Making, Practicing and Performing a Museum Douglas Booth 10. Lest We Forget: Public History and Racial Segregation in Baltimore's Druid Hill Park Jaime Schultz. Conclusion Murray G. Phillips
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".