Bibliographic record
Abstract
There is extensive academic attention on the effects of counterterrorism policy on the Muslim population. My paper goes further by providing an analysis of the intersectionality of religion, race, gender, and the impact of counterterrorism policy, namely ‘Prevent’. I focus upon understanding Muslim women’s experiences concerning the UK’s counterterrorism strategy Prevent, with a theoretical framework of Critical Race Feminism. My research demonstrates the UK government’s incorporation of Muslim women into countering violent extremism policies and how this categorises Muslim women as a tool within deradicalisation. I directly address the gap between feminist research and the lived experiences of Prevent for Muslim women in post-16 education. This is achieved by drawing upon the qualitative experiences of Muslim women in further and higher education in the UK. Through an empirical exploration of focus group and interview data, my PhD paper is one of the first to offer insights into Muslim women’s feelings surrounding how Prevent operates within the UK’s post-16 education sector. To aid this exploration, Critical Race Feminism is used as a theoretical framework to advance the discussion of intersectionality. Within the data collected, certain themes were evident such as: the self-censoring of students; the responsibilization of Muslim women and gendered Islamophobia. The findings state that there is a gendered impact of the Prevent strategy within the UK’s post-16 education sector. This paper should be added to the context of debate about the future of Prevent (if any), and to existing work that discusses the securitisation of racialised people.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".