Bibliographic record
Abstract
Responding to the needs of victims of Islamophobia IntroductionSupport for victims of crime is a fundamental part of a civilised justice system.However, in the current climate of austeritywith the police, courts, prisons, probation and support services facing significant financial cutsthe criminal justice system in the UK falls short of meeting the different and changing needs of communities across the country.As I write this chapter, the police service face a 20 per cent cut in their budget.Undoubtedly, this reality challenges the capacity of police forces to tackle crime, and raises concerns about the quality of service offered to victims of crime.Broadly speaking, victims often need emotional and practical support to recover from the consequences of crime and support services should aim to achieve this outcome.Criminal justice practitionersparticularly those based in diverse communitiesmust have sufficient knowledge and understanding of the specific needs of their clients (Ahmed, 2009).This a contributing factor to offering a more responsive service, which is accessed by the so-called 'hard-to-reach' or 'hidden' communities.Crime, even when seemingly 'low level', can have a devastating impact upon victims, particularly where a person is deliberately or persistently targeted.This should be taken into consideration when support is provided to victims of hate crime, where they are targeted on their actual or perceived disability, race, religion, gender identity or sexual orientation.Against this background, Muslims emerge as the largest faith group experiencing hate crimes (Ahmed, 2012).In a post-9/11 climate, there is an increase in violent attacks targeting Muslims, those perceived to be Muslims, and mosques in the West.In the British context, for example, there has been a rise in violent assaultssome fatalon British and other Muslims living in the UK, in verbal and physical attacks towards Muslim women who wear headscarves (hijab) and face veils (niqab), and in the alarming growth in the number of mosques, cemeteries, Islamic centres and Muslim properties that have been the targets of criminal damage, such as graffiti and arson attacks (Engage, 2010).The establishment of, and subsequent demonstrations by, the English Defence League have contributed to this reality of a rising anti-Islamic, anti-Muslim hostility.Similarly, the British National Party has launched a highly explicit Islamophobic campaign on the basis of resisting the 'Islamification of the UK'.Since November 2012, a new far-right political party called 'True Brits', which consists of former members of the British National Party, operates throughout the UK.In Europe, support for far-right political parties and street-based movements is also on the increase (Bartlett, Birdwell and Littler, 2011), whilst Islamophobia is becoming increasingly 'institutionalised'.Correspondingly, Switzerland has prohibited future construction of minarets on their soil while France, Belgium and Italy have criminalised the Muslim veil through legislation, which bans the wearing of the face veil in public places.Opposition to the face veiling, and indeed Islam at large, encompasses calls to implement similar legislation in Spain, the Netherlands, Scandinavia, Germany, Canada and Australia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".