Discourses of dehumanization: enemy construction and Canadian media complicity in the framing of the war on terror
Bibliographic record
Abstract
This paper examines the Canadian news media’s coverage of the wars in Afghanistan and Iraq. In particular, Canadian newspaper headlines are examined for the way in which an image of the “enemy” is constructed and framed in dominant media discourse. An analysis of the data reveals a pattern of dehumanizing language applied to enemy leaders as well as Arab and Muslim citizens at large in the media’s uncritical reproduction of metaphors that linguistically frame the enemy in particular ways. Particularly, the paper argues that the Canadian media have participated in mediating constructions of Islam and Muslims, mobilizing familiar metaphors in representations that fabricate an enemy-Other who is dehumanized, de-individualized, and ultimately expendable. This dehumanizing language takes the form of animal imagery that equates and reduces human actions with sub-human behaviours. This paper argues that the repeated use of animal metaphors by monopoly media institutions constitute motivated representations that have ideological importance. The consequences of these representations are more than rhetorical, setting the stage for racist backlash, prisoner abuse and even genocide.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.032 | 0.032 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".