Data: Does bottom-line pressure make terrorism coverage more negative? Evidence from a Twenty Newspaper Panel Study.
Bibliographic record
Abstract
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. [1] Miller, E., LaFree, G., Dugan, L. (2014). Global Terrorism Database (GTD). Retrieved from https://www.start.umd.edu/data-tools/global-terrorism-database-gtd. [2] American Presidency Project. (2012). General Election Editorial Endorsements by Major Newspapers. Retrieved from https://www.presidency.ucsb.edu/statistics/data/2012-general-election-editorial-endorsements-major-newspapers. [3] Gallup, Inc. (2014). Media Use and Evaluation. Retrieved from https://news.gallup.com/poll/1663/Media-Use-Evaluation.aspx
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".