Canadian Discourse and Emotions on Terrorism: How Canadian Prime Ministers Speak about Terrorism since 9/11
Bibliographic record
Abstract
This paper analyses the character of the discourse and emotions invoked in speeches delivered by prime ministers of Canada from the 9/11 terrorist attacks up until now. There is increased recognition in academic literature of the need to study emotions, because people are not rational beings and they base their decisions on feelings. Especially the discourse on terrorism is often emotional. The paper argues that there is a need to study the discourse on terrorism and emotions in them, because if the discourse is manipulative it can lead to adoption of counterterrorism measures that are considered ineffective or even counterproductive. This paper attempts to fill the gap in academic literature on terrorism discourse, which usually focuses only on the United States and United Kingdom, by providing a study of Canadian discourse on terrorism. The paper presents an analysis of speeches delivered by Jean Chrétien, Paul Martin, Stephen Harper and Justin Trudeau conducted in NVivo. It finds that each of these prime ministers attempts to influence emotions to some extent to gain support for their counterterrorism policies by invoking emotions such as fear or hate. However, there are also some more calming and less emotional features of the speeches.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".