Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».