Governing Tomorrow’s Terrorists Today: Counter-Radicalization, the Security Complex and Muslims in Contemporary Governmentality
Bibliographic record
Abstract
In grappling with the terrorist threat, states, together with security agencies and governmental bodies rely upon neo-Orientalist constructions of Islam to detect symptoms of the 'known' terrorist that legitimate counter-radicalization policies.Drawing on a governmentality perspective, the thesis unpacks the genealogy of terrorism to elucidate how the terms 'radical' and 'radicalization' have both rendered operative the social construction of risk encircling violence.The thesis argues that the emerging practice of counter-radicalization as a technology of risk has resulted in a permanent state of insecurity.Consequently, in the alleged War on Terror, certain groups are protected and 'Others' subject to scrutiny and stigmatization, particularly Muslims.The thesis goes on to analyze the practice of counter-radicalization in the emerging War on Terror, arguing that its pre-emptive logic legitimates the managing of risks based on future threats.It is posited that a shift from a pre-emptive approach to happening or substantively-developed threats might eschew managing future risks.I am particularly thankful to Ross Williamson who so generously contributed to the work presented in this thesis: for reading and commenting on every single chapter, and persistently.Most notably, I would like to thank my family -my parents and my siblings for being my support system, for encouraging my work and aspirations, and for uplifting me even in times of difficulty.Lastly, many thanks to all my friends for the support in my continuous academic journey.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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 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".