Reflections on an Attempt to Do “Environmental Sports Journalism”: The Behind-the-Scenes Story of the Documentary <i>Mount Gariwang: An Olympic Casualty</i>
Bibliographic record
Abstract
Abstract To discuss our experiences producing a short documentary film focused on a sport-related environmental issue – and reflect on our attempts throughout production to “do” what we are calling “Environmental Sports Journalism” (ESJ). Following ESJ principles, and in collaboration with Vancouver-based filmmakers, we produced a short documentary entitled, Mount Gariwang: An Olympic Casualty, about the destruction of an ancient forest for a sport mega-event (i.e., the PyeongChang Olympics). We discuss and reflect on our approach and methods for producing the documentary, and identify key issues faced throughout the process – as we attempted to negotiate the intricacies of documentary work and collaboration between academics and media producers, while attending to a set of principles for producing “Environmental Sports Journalism.” We reflect on strategies used and challenges faced when attempting to produce a short film on a sport-related environmental issue. We note our attempt to: (1) include interview segments with definitions of key concepts and how they are relevant to power relations around sport mega-events; (2) value the lives and voices of local and marginalized people – while noting problems we faced providing adequate context; (3) focus on problems of nonhumans as well as humans – and the challenges we faced including nonhuman issues and perspectives, challenges that reflected the limits of our chosen data collection and reporting techniques; (4) offer some form of hope and identify alternatives around an event that we were critical of; and (5) highlight the complexities of prioritizing social and environmental justice (i.e., taking a side) while attempting to offer what we might think of as “balanced” coverage. This chapter illuminated barriers we faced in our attempts to produce “excellent” coverage, and in going from media critics to critical media producers. Our hope is to inspire reflection on what is possible around the production of “excellent” sport-related environmental journalism, and to contribute to thinking about the pursuit of public sociology through media. Although involvement in documentary-making as academics is not new, our attempt to apply principles associated with environmental journalism to the study of sport-related environmental and social problems is in some ways novel, and therefore our reflections on our experiences are also in some ways novel.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.044 | 0.030 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.020 |
| 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".