Deconstructing the “War of All Against All”: The Prevalence and Implications of War Metaphors and Other Adversarial News Schema in TIME, Newsweek, and Maclean’s
Bibliographic record
Abstract
This study examines and critiques the discursive construction of a Hobbesian “war of all against all” in North American commercial news magazines. The prevalence of war metaphors and related adversarial news schemas is documented over a twenty year period, from 1981 to 2000, through an analysis of TIME and Newsweek, along with their Canadian counterpart Maclean’s. After documenting the pervasiveness of these discursive constructs, the paper discusses the underlying causes and potential consequences of these patterns in commercial news discourse. The paper concludes by asserting that this discursively constructed “war of all against all” is highly problematic and unsustainable in an age of increasing social and ecological interdependence. Accordingly, scholars who are interested in peace and conflict resolution would do well to take into account the role that news discourse and other forms of mass-mediated communication play in the perpetuation of social conflict.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".