Changing work routines and labour practices of sports journalists in the digital era: a case study of Postmedia
Bibliographic record
Abstract
This article contributes to an emerging body of research that examines the transformation of sport, journalism and media practice in the digital era as part of what Raymond Williams has called the ‘long revolution’ of communications, culture and democracy. In so doing, we explore how Canadian sports journalists have attempted to make sense of, and negotiate their roles within, the practice of convergent sports journalism and the ascension of new online journalism values in the Postmedia Network. We examine the institutionalization of 24/7 digital sports departments within which Postmedia’s sports journalists labour to produce a continuous flow of coverage of major league sport – at the expense of local amateur events and women’s sport – to secure a digital audience commodity of male readers. We also explore Postmedia’s embracement of outsourced labour and production processes that have further altered the work routines of sports journalists and have undermined quality standards. Finally, we underscore how the expansion of the digital promotional networks of major league sport has contributed to the ongoing historical erosion of the status and influence of sports journalists in the sports–media complex and has spurred the rise of derivative analytical and opinion-driven content.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".