Media Constructions of Responsibility for the Production and Mitigation of Environmental Harms: The Case of Mercury-Contaminated Fish
Bibliographic record
Abstract
Within the literature examining media depictions of crime and the criminal justice system, very little attention has been paid to the ways in which harms to the environment and human health have been constructed. This is not entirely surprising, given that the discipline of criminology has been reticent in addressing environmental harm more generally. This gap in the criminological imagination is beginning to be addressed within the growing field of green criminology, which seeks to focus attention on environmental harms as an important area of criminological investigation. Using a green criminological lens, this paper examines the case of mercury-contaminated fish as depicted in the Globe and Mail and New York Times from 2003 through 2008. Through qualitative content analysis, we examine the construction of responsibility for mercury contamination and for mitigating the attendant risks. We find that, in explaining the contamination of fish, both newspapers problematize the regulation of mercury-releasing industries by the state and pay a great deal of attention to the responsibility the state has to inform the public about the risks. However, little attention is paid to the responsibility of the mercury-releasing industries, the commercial fish industry, and restaurants and supermarkets to protect consumers. Furthermore, media attention is mainly directed at the responsibility of individual consumers – particularly those deemed most at risk of being harmed by mercury contamination – to limit the amount of mercury-rich fish they consume. These media depictions simultaneously foster a sense of individualized responsibility and normalize the risks posed by this environmental hazard, which is made to appear virtually inevitable, something that requires management, partly by the state, but mostly by vulnerable consumers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 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".