Bibliographic record
Abstract
Political violence, including terrorism, can be regarded as a form of (distorted) communication, in which media spectacles play an integral role. Conversely, mass-mediated communication can be regarded as a form of violence, and even terror, in several respects. Media are often propagandistic facilitators to state terror. More broadly, they may help to cultivate a political climate of fear and authoritarianism, contributing to conflict-escalating feedback loops. Even more broadly, beyond media representations, dominant media institutions are arguably embedded in relations of global economic, social, and cultural inequality—constituting a form of structural violence. Notwithstanding its democratic potential, the Internet does not comprise a clear alternative in practice, and neither censorship of terrorist spectacles nor the intensified pursuit of dominant forms of journalistic “objectivity” offer viable ways to reduce the media's imbrication with violence. Three potentially more productive strategies explored in this chapter include reforming the media field from within through the paradigm of Peace Journalism, supporting the development of alternative and community media, and building movements for media reform and democratization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".