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.Table of Contents Abstract ……………………………………………………………………………………......ii Acknowledgements..…………………………………………………………………….……..iii Table of Contents.……………………………………………………………………….……..iv Introduction…………………………………………………………………………………….1 Chapter One -Hegemony: The Process of Gathering Consent to Expel the 'Other'..…….…12The Media, Experts, and Government Officials Roles in Reinforcing Hegemony..…....18 Experts………………………………………………………………………...…..20 Government Officials………………………………………………………….…..
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.034 | 0.033 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".