Sociology of Sport: A Global Subdiscipline in Review
Bibliographic record
Abstract
Sociology of sport: South Africa / Cora Burnett -- Sociology of sport: China / Jinxia Dong, Lingnan Liu -- Sociology of sport: India / Veena Mani, Mathangi Krishnamurthy -- Sociology of sport: Japan / Chiaki Okada, Kazuo Uchiumi -- Sociology of sport: South Korea / Eunha Koh -- Sociology of sport: Aotearoa/New Zealand and Australia / Chris Hallinan, Steven Jackson -- Sociology of sport: Czech Republic / Irena Slepicková -- Sociology of sport: Finland / Pasi Koski -- Sociology of sport: Flanders / Jasper Truyens, Marc Theeboom -- Sociology of sport: France / Stéphane Héas, Patrice Régnier -- Sociology of sport: Germany and Switzerland / Markus Lamprecht, Siegfried Nagel, Hanspeter Stamm -- Sociology of sport: Hungary / Tamás Dóczi, Andrea Gál -- Sociology of sport: Italy / Caterina Satta -- Sociology of sport: The Netherlands / Annelies Knoppers -- Sociology of sport: Norway, Sweden and Denmark / Jorid Hovden, Kolbjrn Rafoss -- Sociology of sport: Spain / Núria Puig, Anna Vilanova -- Sociology of sport: United Kingdom / John Horne, Dominic Malcolm -- Sociology of sport: Canada / Parissa Safai -- Sociology of sport: English-Speaking Caribbean / Roy McCree -- Sociology of sport: United States of America / Jeffrey Montez de Oca -- Sociology of sport: Argentina / Raúl Cadaa -- Sociology of sport: Brazil / Wanderley Marchi Júnior -- Sociology of sport: Chile / Miguel Cornejo Amestica
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 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 | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".