Marginal voices in the media coverage of controversial health interventions: how do they contribute to the public understanding of science?
Bibliographic record
Abstract
While the media are a significant source of information for the public on science and technology, journalists are often accused of providing only a partial picture by neglecting the points of view of vulnerable stakeholders. This paper analyzes the press coverage of four controversial health interventions in order to uncover what voices are treated marginally in the media and what the relative contributions of these voices are to the stories being told. Our empirical study shows that: 1) patterns of source utilization vary depending on the health intervention and less dominant stakeholders are in fact represented; and 2) the use of marginal voices fills certain information gaps but the overall contribution of such voices to the controversies remains limited. In order to strengthen the media coverage of science and technology issues, we suggest that further research on journalistic practices: 1) move beyond the dichotomy between journalists and scientists, and 2) explore how different categories of readers appraise the meaning and relevance of media content.
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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 | 0.040 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".