Risk and the Media: A Comparison of Print and Televised News Stories of a Canadian Drinking Water Risk Event
Bibliographic record
Abstract
This article explores the utility of using media analyses as a method for risk researchers to gain an initial understanding of how the public may perceive a risk issue or event based on how it is presented and communicated in news media stories. In the area of risk research, newspapers consistently provide coverage of both acute and chronic risk events, whereas televised news broadcasts report primarily acute risk events. There is no consensus in the literature about which news format (print vs. televised) may be better to study public conceptualizations of risk, or if one format (e.g., print) may be used as a surrogate measure for another format (e.g., televised). This study compares Canadian national televised and newspaper coverage of the same risk event: the E. coli contamination of a public drinking water supply. Using a content analysis, this study empirically demonstrates the overall similarity in story content coverage in both televised and print coverage, noting that televised coverage promotes primarily emotional story themes while print coverage tends to also include coverage of analysis and process. On this basis, the research draws two conclusions: 1) given its more comprehensive coverage, newspaper broadsheets may provide a better measure of media coverage of a risk event than televised coverage (if only one format can be studied); and 2) when the risk area of interest is chronic, and/or if the scale of analysis is at a community/local level (i.e., when it is unlikely that archived televised coverage is available), then a researcher may find the print media to be a more useful format to study.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".