PATTERNS OF HATE CRIME - Demos
28PATTERNS OF HATE CRIMEWHO, WHAT, WHEN AND WHERE?University of Sussex and DemosMark A. Walters and Alex Krasodomski-Jones
CONTENTSAcknowledgments2Executive ions44References491
ACKNOWLEDGEMENTSWe would like to thank the Metropolitan Police Service for providing access totheir Crime Reporting Information System and to Trevor Adams, ColinMcloughlin, Craig Johnson, and Steve Goodhew for their help with navigatingthe system. We also thank James Dickson at Palantir for assisting us with dataextraction, and Miranda Barrie at Demos. Finally, we thank David Weir, JeremyReffin, Simon Wibberley, Alex Casey and Abigail Manning at the University ofSussex for their assistance with this project.2
EXECUTIVE SUMMARYIn an age where hate and prejudice transfer seamlessly from onlineconversations to our communities, we have a duty to protect the mostvulnerable among us. Part of this process involves the effective reporting anddistribution of data on hate crimes in our cities and suburbs. Understandingwhere these crimes are taking place, who the targets are and how themes likerace, religion and sexual orientation play a role is essential to creatingawareness of the problems we face, and allows us to take steps towardscreating safe, equitable environments.Demos’ partnership with the University of Sussex allowed us to take a look atdata collected by the Metropolitan Police Service with the ultimate goal ofidentifying the existing targets of hate crime, and assisting the Police Service intheir efforts to improve the ways in which hate crimes are recorded. Currentmethods enable officers to flag transgender hate crimes but do not allow themto record the gender identities of either suspects or victims as transgender ornon-binary, creating a significant gap within the data. When the most at-riskmembers of our society are not adequately included in hate crime records,they are effectively silenced. By updating existing systems of classification,there is an opportunity to better identify and protect minority communitieswithin the UK.Overall, we found that 84% of recorded hate crimes were based on race, 8%on sexual orientation, 7% on religion and less than 1% were disability ortransgender related. In line with current criminological research, a majority ofaccused perpetrators (76%) were men while only 21% were female. From this,we concluded that gender may impact the strands of hate crime that occur.Another strong trend that emerged was the race of both the offenders andthe victims; a majority of hate crime offenders (66%) were White, while mostvictims (86%) are from non-White British ethnic backgrounds.3
Location emerged as a strong indicator, with recorded crimes reaching ashigh as 449 in the City of Westminster and as low as 63 incidents in Sutton. Themajority of hate crimes involving an accused perpetrator occurred on a publicstreet (40%) or in a public building (34%), which shows that the most commonlyrecorded crimes occur in public spaces. The high number of incidents in theseareas is most likely due to the number of witnesses available.The extreme variation between recorded incidents in London boroughs maybe due to the different demographic makeup across Boroughs. However, it isalso likely to be the result of varying standards in applying MPS investigationand reporting practices across London. The stark differences in recorded hatecrimes between some neighbouring boroughs highlights the need for the MPSto review police officer understanding of the College of Policing guidance onhate crime and its own recording practices for this type of offence acrossLondon.It is evident that hate crimes in the UK remain a key issue, presenting anopportunity for updated recording practices across the board. While incidentsin public spaces are widely reported, it is important to note that there is a lackof data showing the rates of crimes occurring behind closed doors.Encouraging community participation, updating classification systems toinclude transgender and non-binary people and working to share investigativestrategies to develop a consistent standard will allow for a more efficientreporting system for hate crime. As the quality of recording increases, policecan better identify ‘hotspots’, and other situational factors that are key toeffectively policing hate crimes.4
INTRODUCTIONHate crimes have become of increasing concern for police services acrossEngland and Wales over the past few years (HMICFRS 2018). The impacts ofhate crime have been well documented by studies in England and Waleswhich have highlighted how incidents frequently leave victims feelingvulnerable, anxious, isolated and fearful of further attacks (see e.g.Chakraborti et al. 2014; Paterson, et al. 2018; Williams and Tregidida 2013). Lessinformation is available on the perpetrators of hate crimes and on thesituational variables that are linked to such incidents. While police acrossEngland and Wales have collated substantial records on hate crime offending,few have analysed this data to understand patterns and to gain betterintelligence (HMICFRS 2018). In order to better understand hate crime andhow the criminal justice system can best respond to it, we need to know whois committing such offences, what types of offences are most common, andwhere and when offences are likely to be committed. This report aims to fill thisgap in research knowledge by presenting quantitative data extracted fromdetailed reports of hate crime offenders who had been arrested and chargedby the Metropolitan Police Service (henceforth, MPS).About this reportThis report analyses data taken from the Metropolitan Police Service’s CrimeReporting Information System (CRIS) on hate crime, as part of an 18-monthstudy entitled Policing Hate Crime: Modernising the Craft, jointly funded byHEFCE and the College of Policing. The project included multiple partners atthe University of Sussex, Demos, the Metropolitan Police Service and Palantir.Data on recorded hate crimes was extracted from CRIS using Palantir softwareover a two-year period starting from August 2014 – May 2016. Hate crimeincidents recorded by the Metropolitan Police Service include:Any criminal offence which is perceived, by the victim or any other person,to be motivated by a hostility or prejudice based on someone’s5
(perceived) race, religion, sexual orientation, disability or because theyare (perceived to be) transgender. (College of Policing 2014: 4).This provided the research team with 31,141 recorded hate crime offences.The total number of offences was then filtered by selecting only those reportswhich contained information about individuals marked as an “accused”.Within CRIS, records containing an “accused” refer to cases where aperpetrator has been identified, an investigation has taken place and therehas been a “criminal justice outcome” (outcomes include, inter alia, accusedcharge/summoned, caution, community resolution, prosecution not in publicinterest). This means that records containing an “accused” have extensiveinformation, including victim and witness statements, that is based on aninvestigation and collation of evidence regarding the reporting of a criminaloffence that has been flagged as a “hate crime”. This left us with a total of6070 recorded hate crime cases from which the majority of our analyses arebased. In this report we refer to individuals labelled as an “accused” in CRIS as“accused perpetrators” of hate crime.Below we provide a detailed overview of the types of hate crimes that occurin London along with the common situational features and personalbackground characteristics of accused perpetrators for each of the fiverecognised strands of hate crime (e.g. race, religion, sexual orientation,disability and transgender). The data presented below will also show whetherthere are differences in the dynamics of hate crime between and across typesof hate-motivation. The analysis of this data has enabled us to provide ageneral picture of the circumstances in which different strands of hate crimeoccur, the types of incidents which are most common, the areas wheredifferent types of hate crime occur, the background details of individuals whotypically commit different types of hate crime offences, and the types ofrelationships that are likely to exist between victims and accused perpetrators.In other words, what emerged from this study is a detailed analysis of the who,what, when and where of hate crimes.6
Aims and objectivesThere were four general aims of the study: To aid the identification and correct tagging of hate crimes by policeofficers using CRIS To assist officers tasked with investigating hate crime by helping officersto understand typical situational features and personal characteristics ofhate crime offenders across different hate-motivated crimes To improve resource deployment in tackling hate crime across policeboroughs by highlighting when (times and days and months) and where(borough locations) where different hate crimes occur across London To enhance broader knowledge about the nature and dynamics ofhate crime as they may occur across England and Wales7
METHODOLOGYThe data was extracted from the Metropolitan Police archives on CRIS andpresented for analysis in CSV format as three spreadsheets. The spreadsheets,respectively, gave details on: incidents (N 6070, victims (N 7343, suspects(N 6981), and accused perpetrators (N 6426). Victims of hate crime areindividuals who have been identified by the police as having been the victimof a hate crime. It is MPS policy that a hate crime be recorded where a victimperceives the incident to be motivated by prejudice or hate. Note, however,that the data used in this project involve only those cases where a criminaljustice outcome has resulted from an investigation into a reported hate crime.The accused perpetrator file therefore includes details only of individuals whohave either admitted to committing a hate crime or where there is evidencethat they have committed such an offence. Finally, we also use a dataset onsuspects (where relevant) which includes data provided by victims aboutthose who are alleged to have committed a hate crime. Note that suspectsdo not become “accused” perpetrators on CRIS until the police determine acriminal justice outcome. This means that there are more suspects thanperpetrators in the dataset.Collation of these sheets, such that information on each incident includedboth accused perpetrator and victim details, was essential for some of ourcorrelational analyses. In order to achieve this, some accused perpetrator andvictim data was deleted where there was more than one accused or victimper incident, leaving one ‘candidate’ perpetrator and one ‘candidate’ victimper incident. This process was carried out randomly and further checks werecarried out to ensure that subsequent analyses on a reduced data set would8
not be biased by systematic removal of data. The accused perpetrator datawas fully anonymised so that no individuals could be identified.Once the datasets were prepared for analysis we used Excel sheets to codeand filter the data. Descriptive statistics were then used and results werepresented using either Excel data analytics or the database SPSS. This enabledus to provide an accessible and simplified overview of the nature anddynamics of hate crimes across London. The findings are presented below viaa number of tables, pie charts and bar graphs.9
FINDINGSWho?Who are the accused perpetrators of hate crime? In order to understand thenature of hate crime it is helpful to understand more about the types of peoplewho commit such offences. Age, gender, and ethnic background are allimportant factors in understanding the types of people who commit hatecrimes. We need also to identify whether these variables diverge across strandsof hate motivation (i.e. race, religion, sexual orientation, disability andtransgender) and across types of criminal offence (e.g. threatening andabusive language, assaults, criminal damage etc). The following tablesprovide detailed information on the accused perpetrators of hate crime.Main Hate Crime Type BreakdownWe start with a breakdown of the number of recorded accused perpetratorsby hate crime strand.Figure 1: Hate crime by strand10
Table 1: Hate crime by strandHate 8100.0%strandorientationThere was a total of 6070 recorded hate crimes involving an “accused”perpetrator (i.e. cases involving a criminal justice outcome). These crimes arebroken down in the above table by hate crime motivation. Note that the totalis 6258 (compared to 6070 total crimes): this is because some hate crimes arereported as being two different types of motivation (i.e. racial and sexualorientation). The number of multiple motivations is mostly accounted for by theconvergence of racial hate crimes (which are the vast majority of all hate11
crimes) with religious and sexual orientation incidents. This is presented in Table2 below. The table columns refer to whether the crime was racially motivatedand are split into two sections – either “Y” (it was a racial hate crime) or “N” (itwas not a racial hate crime). The rows refer to whether it was also motivatedby one of the four other strands of hate crime.The findings show that over half (229) of the total 431 religious hate crimes werealso race hate crimes. Out of the 431 recorded religious hate crimes, 294 (68%)were recorded as anti-Islamic, 89 (21%) were antisemitic. The rest were spreadacross Christian (16), Sikh (12), Hindu (8), Jehovah’s Witness (2) and Buddhist (1)religions (a further 11 were unknown). 103 (20%) of the total 504 sexualorientation hate crimes were also racially motivated. Four out of 30 disabilityhate crimes were also flagged as race hate crimes, while 3 of 21 transgenderhate crimes were flagged as race hate crimes. Caution should be usedregarding these latter two groups’ data due to the small numbers involved.Table 2: Race hate crimes flagged also as other strands of hate isability26430Transgender18321orientationAccused perpetrator characteristicsThe total number of accused perpetrators in the study was 6426. This numberis greater than the total number of incidents as some incidents had more thanone accused perpetrator (see Table 7 below).12
GenderThe dataset revealed that 78% of accused perpetrators are male and 22% arefemale, after removing the 3% of cases where gender was missing. This meansthat hate crimes in London are 3.5 times more likely to be perpetrated by aman than a woman. Note that there is no option for the police to recordtransgender or non-binary people.Figure 2: Accused perpetrators of hate crime by genderACCUSED - GENDER48781351MaleFemaleThere were no discernible demographic differences between male andfemale accused perpetrators. For example, male and female accusedperpetrators tend to be of a similar average age, and have similar ethnicbackgrounds. However, a break-down of gender by hate crime type revealedimportant differences in the types of hate motivations demonstrated acrossthe two genders. Most stark was that transgender hate crimes were muchmore likely to be committed by men (86%), while disability hate crimes had aslightly lower percentage of male offenders at 74%.13
Table 3: Hate crime by genderHate crime n16%84%Disability26%74%Sexual orientation15%85%AgeThe mean age of an accused perpetrator for all hate crime strands was 40years old (Mean 39.6; SD 14). We found that 68% of accused perpetratorswere between 26 – 54 years old. The most common age (mode) was 36 yearsold. The youngest accused perpetrator was 11 years old and the oldest was 89years old. Just 3% of accused perpetrators were under 18 years old (192 out of6631). The majority of accused perpetrators fell within the age range of 31-50years old. Breaking the age ranges down by hate crime strand did not revealany major differences.Age Quartiles11yr30% 30yr1st31yr46% 50yr2nd51yr21% 70yr3rd71yr2% 89yo4thSelf-Identified EthnicityThe perceived ethnicity of an accused perpetrator is recorded by the policeand where available self-identified ethnicities are also added. Here we useonly the data which is are self-identified in order to provide the most accuratereflection of accused perpetrator ethnicities (resulting in a total of 4700records, leaving 1072 records with missing data due to: “Officer urgentlyrequired elsewhere; Situation involving Public Disorder; Person does notunderstand; Person declines to define ethnicity”). Table 4 below shows the14
frequencies and percentages of those records where self-reported ethnicitieswere logged. Combining the three categories (“White – Any other Whitebackground”, “White – British”, “White – Irish”) we found that White accusedperpetrators constitute 66.5% of all hate crime. This percentage is higher thanthe estimated White population in London, as reported after the 2011 censuswhich was calculated at 59.8% – though it should be noted that this data isnow seven years old. The second largest ethnic group of accused perpetratorswas Black (including Black Caribbean, Black African and Black other) at 17.4%;this is also proportionately higher than the census data which put the Blackpopulation at 13.3%. Asian accused perpetrators account for 9% of recordedhate crimes, which is half of the calculated population (18.4%) of Asian peoplein London.Table 4: Ethnicity of hate crime accused perpetratorsEthnicityNPercentageAsian – Indian1092.3%Asian – Pakistani801.7%Asian – Bangladeshi721.5%Asian - Any other Asian 1663.5%backgroundBlack – Caribbean2765.9%Black – African3056.5%Black - Any other Black 2395.1%backgroundMixed - White and Black 751.6%CaribbeanMixed - White and Black 250.5%AfricanMixed - White & Asian150.3%Mixed - Any other Mixed 781.7%background15
ChineseorOther- 130.3%ChineseAny other Ethnic group1192.5%White – British218846.5%White – Irish2595.5%White - Any other White 68114.5%backgroundTotal4700100%Table 5 below provides the percentages of self-identified ethnicities by hatecrime strand. The percentages of accused perpetrators from the differentethnic groups for each strand of hate crime are broadly the same. However,there were some key differences across the strands worthy of highlighting. Forexample, accused perpetrators with Asian backgrounds were the least likelyto be arrested for a hate crime, except for transgender hate crimes – with 20%of transphobic incidents recorded as involving an Asian accused perpetrator(note the small numbers involved here). Accused perpetrators of disability hatecrime were committed by White accused perpetrators (75%) and Blackaccused perpetrators only (though the numbers here are again very small).Finally, 20% of sexual orientation hate crimes and 29.4% of transgender hatecrimes were committed by Black accused perpetrators.16
StrandWhite - BritishRace1Asian - PakistaniBlack - AfricanBlack - Any other Black backgroundBlack - CaribbeanChinese or Other - ChineseMixed - Any other Mixed backgroundMixed - White & AsianMixed - White and Black ate Crime2121Mixed - White and Black CaribbeanWhite - Any other White backgroundWhite - BritishWhite - IrishTOTAL10orientation72122Table 6: Ethnicity of accused perpetrators by hate crime strand .614.547.45.8%%%%%%%%%%%%%%%%12617White - Irish5224Mixed - White and Black Caribbean13766Mixed - White and Black African16Mixed - White & Asian10Mixed - Any other Mixed background4Chinese or Other - Chinese5Black - Caribbean94Black - Any other Black background59Black - AfricanAsian - Indian13Asian - PakistaniAsian - Bangladeshi109Asian - IndianTransgender8Asian - BangladeshiReligionAsian - Any other Asian backgroundSexualAsian - Any other Asian backgroundStrandAny other Ethnic groupRaceWhite - Any other White backgroundAny other Ethnic groupTable 5: Ethnicity of accused perpetrators by strand of hate crime (frequency)Hate Crime6659192340027667481621376215120731514338817
%%%8%%%%%%%%%%Number of suspects and victims per hate crimeFor each crime record a total number of suspects (and in turn accusedperpetrators) is added to the reporting system. Table 7 below shows thefrequencies and percentages of crimes with varying numbers of “suspects”(individuals not yet arrested or charged) in cases where at least one personwas apprehended, arrested and charged with an offence. We use data onsuspects here which is a more accurate reflection of the number of peopleinvolved in the commission of the offence, compared to the number of“perpetrators” which includes only those who are identified and a criminaljustice outcome recorded.The records indicate that the majority of hate crimes involve one suspect. Lessthan 10% of hate crimes had more than one suspect. Note that in eight casesthere were zero suspects. This is because the incident involved propertydamage crimes or an incident of a similar nature.Table 7: Number of suspects per recorded hate crimeNumber 4310.51%5160.26%18
2%Table 8: Number of suspects by hate crime 00.00%00.00%2900.00%00.00%10.23%00.00%00.00%The next table shows the number of victims involved per crime record. 68% ofcrimes involved just one victim and 26% involved two or more.Table 9: Number of victims per recorded hate crimeN of Victims N of cases%03746.2141266819
70100Witnesses to hate crimes are pivotal to the investigation process and theirevidence is typically relied upon when making decisions to arrest and latercharge. The table below shows the number of witnesses for recorded hatecrimes resulting in arrest, charge and sanction. The table shows that 42% ofcases involved more than one witness.Table 10: Number of witnesses per recorded hate crimeN of witnessesN of 6460.87260.48110.220
970.110101120141017202410Total6070100There is a significant positive correlation between the number of accusedperpetrators and number of witnesses (r .12, b(1, 6070), p .001). There is alsoa significant positive correlation between the number of accused perpetratorsand number of victims (r .1, b(1, 6070), p .001). The correlation coefficient(r) is positive (which means as one goes up so does the other); though bothare small. Most notable is that transphobic crimes were markedly more likely tobe perpetrated by fewer people, though caution is needed in interpreting thisresult due to the small numbers involved.Suspect known by victim?Information about whether a suspect was known to the victim was listed in aseparate but linked dataset. This dataset contained 6981 records. Out of thetotal number of suspects, 21% of victims stated that they knew their suspectsomehow.Table 11: Number of suspects known to victimSuspect Known by Victim Number of Cases PercentageN550478.921
Y147721.2Total6981100Suspect known how?The type of relationship between victim and suspect was also listed in thedataset. The table lists the types of relationships, in order of frequency. We cansee that neighbours of victims were by far the most likely relationship for a hatecrime, followed by being an acquaintance of the victim.Table 12: Relationship types between victims and known suspectsRelationship typeN%Neighbour of victim62943%Acquaintance of victim27919%Suspect known by victim in another way18613%Client of victim493%Person living in the same premises413%Care provider of victim403%Mother of victim352%Friend of victim312%Patient of victim221.5%Victim’s residential social worker211.5%Colleague of victim181%Father of victim171%Victim’s non-residential social worker121%Employee of victim80.5%Uncle of victim70.5%Boyfriend of victim60.5%Ex-Boyfriend of victim60.5%Attends the same school as the victim60.5%22
Cousin of victim50.5%Husband of victim50.5%Stepfather of victim50.5%Doctor of victim4 0.5%Brother of victim4 0.5%Student/Pupil of victim4 0.5%Business associate of victim3 0.5%Employer of victim3 0.5%Son of victim3 0.5%Ex-Employee of victim2 0.5%Foster Mother of victim2 0.5%Aunt of victim2 0.5%Niece of victim2 0.5%Father-in-Law of victim2 0.5%School worker at victim’s school2 0.5%Criminal Associate of victim1 0.5%Tradesman of victim1 0.5%Guardian of victim1 0.5%Nanny of victim1 0.5%Wife of victim1 0.5%Girlfriend of victim1 0.5%Ex-Girlfriend of victim1 0.5%Step Mother of victim1 0.5%Daughter of victim1 0.5%Common law husband of victim1 0.5%Foster Father of victim1 0.5%Grandfather of victim1 0.5%Nephew of victim1 0.5%Babysitter of victim1 0.5%Au pair of victim1 0.5%23
Teacher of victim1 0.5%Total1477100%Relationship by hate crime strandOut of the 1477 cases recorded as relationship known, there were 1385 recordswhich contained information pertaining to the hate strand (leaving missingdata of 160 records). Out of the 1385 records we are able to see whether therelationship types vary by hate strand by dividing the total number of victimsknown to the suspect by the total of all cases recorded by each strand. Bydoing this a different picture emerged for the relationships that exist acrossdifferent types of hate crime. Most striking was that 45.7% of disability hatecrime victims knew the suspect involved in their cases, while only 14.2% ofvictims of religious hate crime stated that they knew the suspect. Note,however, the small numbers for disability and transgender records.Table 13: Suspects known to victim by hate crime strandHate strandKnown to victim% of total(total cases)Race1121 (5966)18.8%Religion74 (522)14.2%Sexual orientation164 (612)26.8%Disability21 (46)45.7%Transgender5 (31)16.1%24
47.24SexualRace (#)Doctor of victim40.36%Patient of victim191.69%44541Client of victimNeighbour of victim-%Friend of victim171.52%10.09%1Employer of victimEmployee of victimBusiness Associate of34%1045.95733.33%TransgenderDisability (%)-(#)Disability (#)-2(%)Religion (#)-0.27%orientation-3(#)Sexual-Cousin of victimorientation1.23%Race (%)Religion (%)Relationship Type(%)Table 14: Relationship type by hate crime ----30.27%-------10.09%--------victimCriminal Associate ofvictim419.05%Ex Employee of victimColleague of victimAcquaintance of 22.97%Tradesman of victim10.09%Person living in same312.77%211.87%9Girlfriend of victimEx Girlfriend of -------Mother of victim10.09%--------Foster Mother of victim10.09%--------Niece of victim10.09%1.23%------Husband of victim20.18%1.35%----Boyfriend of victim20.18%--------Ex Boyfriend of victim20.18%--------Son of victim30.27%--------Father in Law of victim10.09%--------Nephew of victim10.09%--------Teacher of Victim10.09%--------premises (flat/housemate)Victim’s ResidentialSocial WorkerVictim’s NonResidential SocialWorker210.61%125
School Worker at10.09%--1Student/Pupil of Victim40.36%--Attends the %----1114.864.76%-----Victims School-School as The VictimSuspect known byvictim in another way%Victim’s Care e of background information on offenders provides part of a picturethat helps us to better understand hate crime offences. However, we alsoneed to understand what types of criminal offences are most common. Dothese types of offences change across and between different hatemotivations? What can we learn about the nature of hate crime byunderstanding, in more detail, the types of offences that are most commonlycommitted?Offence typesFrom the 6070 crime records, there were 120 different named offences listed.The frequency with which each one occurs ranges from 1 to 2396. In order topresent this data visually we have only included offences that constitute morethan 1% of cases. This produced 11 offence types. The graph below shows howfrequently each offence type occurs. By far the most common type of offencerecorded were racially or religiously aggravated intentional harassment, alarmand distress, making up almost half of all recorded hate crimes.Figure 3: Offence types26
217116PUBLIC ORDER OFFENCE S5 POA 86129PUBLIC ORDER OFFENCE S4 POA 86207GBH/SERI OUS WOUNDI NGR ACI AL FE AR / P R O V O CAT I O N V I O L E N CE237ABH278CO M M O N ASSAU L T322R ACI AL L Y / R E L I GI O US AGG ABH406PUBLIC ORDER OFFENCE S4A POA 86R ACI AL L Y / R E L I GI O US AGG FE AR O F VR ACI AL L Y / R E L I GI O US AGG ASSAU L TR ACI AL L Y / R E L I GI O US AGG H AR AS S M E N TOFFENCE TYPE239670659The figures below show the total numbers by offence type for each of thestrands of hate crime motivation. Due to the length of the table we includeoffences with five or more offences recorded for race hate crimes.Figure 4: Offence type for race hate crimes27
WO U N
Hate crime incidents recorded by the Metropolitan Police Service include: Any criminal offence which is perceived, by the victim or any other person, to be motivated by a hostility or prejudice based on someone's 6 (perceived) race, religion, sexual orientation, disability or because they are (perceived to be) transgender.
8 Hate Crime Laws: A Practical Guide hate crime laws are not used, it diminishes respect for all laws and weakens the rule of law. An effective criminal law response to hate crimes requires considering how a hate crime law will work in practice, and whether drafting choices make the law more or less easy to understand and use.
An analysis of hate crime events motivated by an anti-Asian bias reported to the DOJ in the last fve years (2016 through 2020) found that: Te number of reported anti-Asian hate crime events in California has increased since 2016 Te number of reported anti-Asian hate crime events increased by 107% in 2020, from 43 in 2019 to 89 in 2020.
CONSULTATION PAPER . 2 . 3 Foreword Hate crime is a complex concept and a proper understanding of what it is can often be made more difficult by its frequent elision with other related concepts such as hate speech and incitement to hatred. In 2017 the Northern Ireland Policing Board (NIPB) noted that “the term ‘hate crime’ appears routinely in government policy and is used frequently in .
being the victim of crime and feel at risk of being a victim of hate crime. Both the experience and fear of homophobic hate crimes and incidents have a dramatic impact on the quality of life of millions of LGB people. At the time of the survey, YouGov had information on the sexual orientation of only a proportion of their panel members, and 99
there are real-world consequences to public debates, and our new report shows how trans victims of assault and abuse are being left behind. Recorded transphobic hate crime has doubled in the last three years – and we know from this report that only 1 in 7 trans people are reporting their experiences. This is reflected in the escalating scale, severity and complexity of hate crime cases we .
A Comparative Analysis of Hate Crime Legislation . A Report to the Hate Crime Legislation Review . James Chalmers and Fiona Leverick . South Africa and ten jurisdictions in the United States of America (the jurisdictions selected for analysis being those which expressly protect characteristics
AQA A LEVEL SOCIOLOGY BOOK TWO Topic 1 Functionalist, strain and subcultural theories 1 Topic 2 Interactionism and labelling theory 11 Topic 3 Class, power and crime 20 Topic 4 Realist theories of crime 31 Topic 5 Gender, crime and justice 39 Topic 6 Ethnicity, crime and justice 50 Topic 7 Crime and the media 59 Topic 8 Globalisation, green crime, human rights & state crime 70
that black holes thermally radiate and calculated the black-hole temperature. The main feature of the Bekenstein–Hawking entropy is its proportionality to the area of the black-hole horizon. This property makes it rather different from the usual entropy, for example the entropy of a thermal gas in a box, which is proportional to the volume. In 1986 Bombelli, Koul, Lee and Sorkin [23 .
