Where’s all the ‘good’ sports journalism? Sports media research, the sociology of sport, and the question of quality sports reporting
Bibliographic record
Abstract
Across newsrooms and journalism schools, questions as to what constitutes or ‘counts’ as excellent reporting are currently inciting much debate. Among the various frameworks being put forward to describe and encourage ‘excellent’ journalism in its various forms, sport is seldom mentioned – a legacy perhaps of its perennial dismissal as trivial subject matter. This essay grew from our curiosity as to whether the reverse was also true: that is, whether and what those who study sports journalism and sports media – in particular sociologists of sport – have contributed to understandings of ‘best’ and even excellent journalistic practice. We identified and analysed 376 articles from eight leading scholarly journals that feature sports media research with the aim of examining instances where ‘excellent’ sports reporting was either highlighted, described or advocated. After outlining the major themes that emerged from this analysis, we reflect on why so few of the sampled articles explicitly advise on what best practice sports journalism might look like – especially when it comes to coverage of the sport-related social issues that sociologists of sport tend to focus on – and why so little theoretical attention has been afforded to the question of excellent sports journalism more generally. While there are good sociological reasons for focusing on problematic sports reporting, on structural and systemic issues in which media are implicated, and on producing alternatives to hegemonic sports media, we conclude that it is high time for instances of excellent sports journalism to be afforded the theoretical and empirical attention long granted to their ‘bad’ journalistic counterparts.
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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 | 0.047 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.042 | 0.030 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".