Data: Does bottom-line pressure make terrorism coverage more negative? Evidence from a Twenty Newspaper Panel Study.
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
<p>We use an original panel dataset to explore the impact of economic pressure on the way journalists report terrorism. This dataset combines data about terrorist attacks in the U.S.[1], presidential endorsements by newspapers[2], ownership and profit information, and public distrust of the media[3] with tone scores for randomly selected articles on terrorism from 20 newspapers spanning 1997 to 2014. This publication includes a Stata data file (in dta and csv formats) as well as an appendix with a description of variables and selected tables and figures.</p> <p>&nbsp;</p> <p>[1] Miller, E., LaFree, G., Dugan, L. (2014). Global Terrorism Database (GTD). Retrieved from <a href="https://www.start.umd.edu/data-tools/global-terrorism-database-gtd">https://www.start.umd.edu/data-tools/global-terrorism-database-gtd</a>.</p> <p>[2] American Presidency Project. (2012). General Election Editorial Endorsements by Major Newspapers.<br /> Retrieved from <a href="https://www.presidency.ucsb.edu/statistics/data/2012-general-election-editorial-endorsements-major-newspapers">https://www.presidency.ucsb.edu/statistics/data/2012-general-election-editorial-endorsements-major-newspapers</a>.</p> <p>[3] Gallup, Inc. (2014). Media Use and Evaluation. Retrieved from <a href="https://news.gallup.com/poll/1663/Media-Use-Evaluation.aspx">https://news.gallup.com/poll/1663/Media-Use-Evaluation.aspx</a>&nbsp;</p>
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| 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