The terror: an examination of the emerging discourse on terrorism and its media representations
Bibliographic record
Abstract
This thesis examines the recently emerging discourse on terrorism from a Canadian perspective. I utilize Michel Foucault's ‘discursive formation’ as a method, which allows me to examine how the discourse emerges. I focus on photographs and how they are used to illustrate the threat of terrorism to the ‘Canadian public’. I show that two categories develop, the innocent Canadian and the evil terrorist. One is represented as either a victim or a terrorist but never both. In this discourse the Arab and Middle Eastern race, brow skin and the Muslim religion all come to represent the totalizing category—terrorist. The idea of the Arab as terrorist existed prior to the September 11th terrorist attacks so what I am examining is how this idea is cemented through discursive formation and visual representations and further, how it is codified through the implementation of Bill C-36, the Anti-Terrorism Act.
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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 | 0.011 | 0.013 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".