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
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.015 |
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".