Discourses of fear and victimization: the impact of national security legislation on the Tamil Canadian community
Bibliographic record
Abstract
National security discourses have a significant impact on migrant, refugee and immigrant communities. This thesis will address the impact that national security legislations have on vulnerable ethnic communities using the Tamil community in Canada as a case study. In highlighting concerns about rashly buying into the dominant discourses of terrorism and security, critical insights into how laws and policies impact community groups and society as a whole will be raised by exploring the discourses of fear and victimization. In particular, two important questions will be addressed. Firstly, how does the dominant discourse on fear of terrorism in national security legislation impact on the victimization of community groups, such as Tamil-Canadians? And secondly, what methods should be employed by communities so that the cycle of fear and victimization can be broken to enable the community to act with agency and resist these dominant discourses?
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.052 | 0.029 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.008 |
| 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".