A Comprehensive Analysis of Breast Cancer News Coverage in Leading Media Outlets Focusing on Environmental Risks and Prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breast cancer has a high profile in the news media, which are a major source of information for cancer patients and the general public. To determine the nature of breast cancer news coverage available to audiences, particularly on the topics of environmental risks and prevention, this content analysis measured a broad array of dimensions in 231 stories appearing in nine leading newspapers, newsmagazines, and television networks in 2003 and 2004. One fourth of all stories reported on various risks such as hormone replacement therapy (HRT) use. Very few items specifically addressed risks related to controllable lifestyle practices such as prepubertal obesity or chemical contaminants in the environment. About one third of the stories included prevention content, primarily focusing narrowly on use of pharmaceutical products. Little information described risk reduction via other individual preventive behaviors (e.g., diet, exercise, and smoking), parental protective measures, or collective actions to combat contamination sites. The more traditional categories of prevalence, detection, and treatment were featured in one third, one quarter, and two fifths of the news items, respectively. There were twice as many stories featuring personal narratives as statistical figures, and two thirds of all the news items cited expert medical professionals, researchers, or organizations. Implications of these findings and directions for future research are addressed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it