Bibliographic record
Abstract
Since most of our knowledge about current events such as wars in foreign countries comes via news reports, the ability to recognize bias in news media is an important skill. Linguistic discourse analysis is one way in which one can identify the unconscious biases evident in otherwise objective news. Patterns in positive and negative word choices can show which news actors a news organization identifies with and which an organization assumes to be suspect or ill-motivated. The purpose of this study was to find such patterns in word choices from the war in Iraq. To this end, headlines were gathered over a one-month period from an American and a Canadian newspaper, and the violent-connotation in words were evaluated in choices of participant labels and verbs. The frequency of violentlyconnotated words for both sides in the war were compared to body count numbers to determine whose violence the news organizations de-emphasized. The occurrence of explicitly-violent participant labels and verbs showed that both the American and the Canadian newspaper found anti-American violence in Iraq more newsworthy. The violence of the American military and its allies was deemphasized in a manner inconsistent with accepted body count numbers, showing that both news organizations favored the American side of the war.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".