Do the first 10 days equal a year? Comparing two Canadian public health risk events using the national media
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
It has been suggested that the way in which a risk event is presented within the first 10 days of media coverage provides the major frames that will dominate news media presentations of any new information about the event over time. A media content analysis for two prominent Canadian health risk events (the E. coli water contamination event in Walkerton, Ontario, in 2000 and the discovery of Bovine Spongiform Encephalopathy or Mad Cow Disease in a cow in Alberta in 2003) in a major national newspaper was used to examine this hypothesis. For both case studies, the story frames in the first 10 days of coverage were not significantly different than during a full 1-year period following the event, indicating that a 10 day analysis should be sufficient to determine media presentations of the risk events. The increased accessibility of information during this period (as indicated by the greater number of articles on the front page of the newspaper) reinforces the usefulness of looking at the first 10 days to establish dominant frames. However, the 10 day analysis is only reliable for a risk event that remains relatively constant over time provided that media coverage remains high for an extended period and that no new salient issues emerge.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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