A Proposal for an “Environmental Sports Journalism” (ESJ) Approach: Principles and Illustrative Examples From Coverage of the Rio 2016 and PyeongChang 2018 Olympic and Paralympic Games
Bibliographic record
Abstract
This article introduces/rationalizes an attempt to conceptualize “environmental sports journalism (ESJ).” ESJ refers to a set of principles for analyzing and/or reporting on media coverage of sport-related environmental issues—principles intended to support/promote dialogue and nuanced thinking about these issues and about how sports journalism might contribute to environmentally friendly and just outcomes. To clarify features of ESJ and explore benefits/challenges of ESJ, we include illustrative examples of ESJ from media coverage of: (a) polluted harbor water used for the 2016 Rio Olympic and Paralympic Summer Games and (b) the razing of an ancient forest for a ski facility for the 2018 PyeongChang Olympic and Paralympic Winter Games. We conclude with reflections on the potential/limits of ESJ and suggestions for work on sport, journalism, and environmental issues.
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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 | 0.050 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.017 | 0.088 |
| Scholarly communication | 0.035 | 0.032 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".