Exploring mechanisms of whiteness: how counterterrorism practitioners disrupt anti-racist expertise
Bibliographic record
Abstract
Abstract This article situates the subject of the academic–practitioner (AP) exchange within an International Relations-orientated critique of the imperial dynamics of counterterrorism practices and racial subjugation. It uses an analytical framework that upholds the significance of racial hierarchy to knowledge production. A key contribution of this article is to situate the AP nexus within the circumstances of liberal democratic counterterrorism regimes, to demonstrate how race becomes meaningful to the knowledge that is produced about Islamophobia. The main argument of this article is that in present policy debates concerning the existence of systemic racism, one of the mechanisms enabling counterterrorism practitioners to regulate the AP exchange is that of institutionalized whiteness. Exploring two scenarios of AP exchanges in the United Kingdom and Canada, where counterterrorism practitioners were challenged to reconcile with academic explanations of Islamophobia as a systemic issue, this article uses colour-line inspired critiques of white logic to identify instances where anti-racist knowledge was subjugated in the name of imperialism. The article finds that in each scenario discussed, practitioners demonstrate trajectories of white logic by contesting the suitability of anti-racist knowledge put forward by academics, on the basis of racial hierarchy and self-aggrandizement. It concludes by discussing how a lack of practitioner–academic consensus continues to affect the dissemination of knowledge concerning systemic racism, thus prompting considerations of what this means for an anti-racist future.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.065 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".