Bibliographic record
Abstract
This MRP was inspired by my ongoing interest in the media’s role in educating the public about current events, specifically how the media’s coverage after the terrorist attack on September 11th, 2001 influenced their audience. The MRP focused on the broadcast news coverage the week after the terrorist attack and how the framing of the attack, influenced who the public’s understanding of who the enemy of the War on Terror. The MRP conducted a content analysis of FOX News and CNN’s 6:00 broadcast news coverage. The MRP found that the media had a tremendous influence over the public at this time and significantly contributed to their understanding of who the enemy was in the war. It also discovered the role that the Bush Administration had in framing the media’s agenda and they used broadcast television to push their own political agenda. The MRP will teach the reader about the overpowerful role the news media can have, especially in times of crises and how the media can shape and present news events to with significant bias. Winston Churchill once said that with great power comes greater responsibility. This MRP teaches about the great responsibility of the news media and how during the news coverage after the terrorist attack, they unfortunately, did not live up to.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".