The role of the media in construction and presentation of food risks
Bibliographic record
Abstract
In this article we examine how and why the media construct food risks, from the perspective of ‘media actors’ (people involved in different types of media) using data from 30 interviews conducted in 2013 with media actors from Australia and the United Kingdom. In modern society, many risks are invisible and are brought to the attention of the public through representations in the mass media. This is particularly relevant for food safety, where the widening gap between producers and consumers in the developed world has increased the need for consumer trust in the food supply. We show the importance of newsworthiness in construction of media stories about food risk using Beck’s ideas on cosmopolitan risk to interpret the data. We note the ways in which the strategies that media actors use to construct stories about food risk amplify the risk posed potentially creating consumer anxiety about the safety of the food system. It is important for food regulators and public health professionals to be aware of this anxiety when presenting information about a food incident so that they can target their message accordingly to decrease anxiety.
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 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".