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
The present study examined the most prominent subject matter in CBC news articles during the COVID-19 pandemic in 2020. The sample was collected through a google web search that read “COVID-19 AND Coronavirus AND CBC news articles AND Alberta.” The 50 most recent news articles that appeared in this search and contained “COVID-19” or “Coronavirus” in relation to Alberta in the article headline were sampled. The article headlines were individually analyzed in a first-phase coding process and then re-examined for common themes. A qualitative content analysis determined that the most prominent subject matter in CBC news articles was statistics (50%) followed by general updates (22%), COVID-19 information (14%), current events (6%), and regulations (4%). An additional category titled ‘other’ (4%) was created for articles with subject matter that was unrelated to these themes. The analysis found that the most prominent news content in CBC news articles was related to fear inducing information which replicates the results from earlier studies on the H1N1 pandemic in 2009.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.001 |
| Scholarly communication | 0.003 | 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