Canada’s Double Standard: How Hegemony, Fear and Post 9/11 Understandings of Security Inform our Perceptions of Who is Deemed ‘Terrorist’
Bibliographic record
Abstract
The terrorist attacks of 9/11 brought the reality of terrorism experienced throughout the Middle East to Western shores. The binaries of 'us' and 'them' were solidified through hegemonic narratives meant to stabilize the country, ensuring that only one type of terrorist, the 'brown' terrorist, is visible. These narratives presented the attacks as a new 'exceptional' threat, painting the world as a much riskier place which mobilized security in different ways. The emphasis on national security in the post 9/11 environment ensures that particular threats ('brown' terrorists) are prioritized, enabling more serious threats to persist while remaining hidden ('white' terrorists). This thesis analyses the differing responses and narratives from the government, media, and experts regarding right-wing extremists and the refugees aboard the MV Sun Sea. Theories of hegemony, fear, and security are drawn upon to explain why, and how, one group is redeemable while the other is condemned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".