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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".