Media Representations and Farmer Perceptions: A Case Study of Reporting on Ocean Acidification and the Shellfish Farming Sector in British Columbia, Canada
Bibliographic record
Abstract
Ocean Acidification (OA) creates corrosive conditions that impact organisms that produce calcium carbonate shells, such as clams and oysters. The Salish Sea, a body of water where much of British Columbia's shellfish farming sector operates, has been growing more corrosive. We present a case study of reporting on OA and the shellfish farming sector in British Columbia, Canada. We convey results from a survey with shellfish farmers and a thematic analysis that sought to understand how the science and local implications of OA were presented in a sample of media articles. All articles employed narratives of crisis, and slightly over 75% conveyed scientific uncertainty. Just over 55% incorporated interviews with one or more of scientists, shellfish sector representatives, and shellfish farmers. Survey findings reveal that respondents saw OA as a threat but often deprioritized it relative to a wider range of operational challenges. We introduce “situatedness” and draw in ideas from “solutions journalism” to expand. While telling stories about people and places is important, we conclude that new opportunities for locally salient climate change reporting stand to be unlocked by looking beyond boundaries typically drawn around “the local” and the sorts of credentials typically ascribed to “environmental experts”.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".