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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".