Bibliographic record
Abstract
Abstract Purpose – This chapter highlights how counter-radicalization, as a manifestation of diffuse securitizing, impacts the work of Muslim civil society organizations (CSOs) in Canada. Methodology – The author presents how Muslim communities and their civil society representatives experience and adapt to the pressures from counter-radicalization policies. Data for the analysis are drawn from 16 semi-structured, anonymized interviews with managers and board members of prominent Muslim CSOs that are based in urban centers in Canada with high density of Muslim populations. Findings – Though counter-radicalization policies are advanced under the rubric of community-orientedness and risk governance, security discourse and practice constructs radicalization as a problem within Muslim communities treating them as suspects who are “potentially radical.” Despite this framing, Muslim CSOs are cooperating with state security agencies in counter-radicalization efforts but are doing so cognizant of the immense power the state exerts over them in such “partnerships.” CSOs are raising questions about the selective nature of security practice which views Muslims as dangerous and violent but fails to fully acknowledge their reality as victims of Islamophobic violence. CSOs are using anti-racism, anti-oppression, and rights-based frames to call out the discriminatory treatment of Muslims under national security. Originality/Value – The author’s study contributes to a community perspective in counterterrorism and counter-radicalization research that is dominated by analyses from “above.” By sharing the experiences of Canadian Muslim CSOs under counter-radicalization, the author illustrates the practice of “diffuse securitizing” and how it limits the work of civil society in liberal democracies.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.014 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".