Assessing the sociology of sport: On public sociology of sport and research that makes a difference
Bibliographic record
Abstract
On the 50th anniversary of the ISSA and IRSS, one of the most influential scholars linking the sociology of sport to policy and social change, Peter Donnelly, considers the prospects for a nexus meshing public intellectualism with the doing of research. Noted in considering the trajectory of the field was an early absence of the sociologist as partisan and the staking of positions that make clear “whose side we are on.” While the critical turn in the sociology of sport has grounded its calling more clearly, challenges remain for the field to be taken seriously, and a key to advancing this is to quicken the pace for sociological knowledge about sport to “filter down” to public knowledge and play a role in influencing policy changes that can help make sport a more equitable and humane place. In considering the future of the field, Donnelly notes the inherently practical value underlying sociological knowledge and calls for sociologists of sport to engage Piketty’s call for public engagement in a way that can make a difference in how people experience sport.
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 | 0.047 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.022 | 0.036 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".