THE INFLUENCE OF CANADIAN SECURITY INTELLIGENCE SERVICES ON THE FORMATION OF RELIGIOUS AND NATIONAL IDENTITIES OF MUSLIM CANADIANS
Bibliographic record
Abstract
This thesis explores the affect that Canadian Security Intelligence Services (CSIS) has on Muslim Canadians. Drawing on concepts of religious and national identity, I explore the ways these identities are shaped and constructed after individuals are approached by CSIS agents. This study presents a qualitative study of the lives of 8 Muslim Canadians and their experiences in both their religious and national communities after being interviewed by CSIS officials. This thesis explores how religious identity is expressed through religious community involvement and how boundaries of community are formed. In particular it examines how interviews with CSIS agents influence individuals to become more or less involved in their religious communities. Further, I discuss some of the implications that interviews with CSIS can have on the community as a whole. National identity presents a more complex and challenging exploration of defining citizenship, nationhood and the role of government. For all of these individuals, their sentiments towards citizenship and their perceived place within Canadian had shifted after being approached by CSIS officials. These changing identities are placed into a larger framework that examines the problems associated with defining Muslim Canadians, Islamophobia, Canada's approach to multiculturalism and Canada's response to terrorism and security. Thus, this thesis examines some of the critical issues that Muslim Canadians face and how these particular topics, in addition to an interview with CSIS agents, have influenced the lives of the individuals in this study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.013 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".