The purpose of this study was to examine the crime of identity theft from the offenders' perspectives. The study employed a purposive sampling strategy. Researchers identified potential interview subjects by examining newspapers (using Lexis-Nexis), legal documents (using Lexis-Nexis and Westlaw), and United States Attorneys' Web sites for individuals charged with, indicted, and/or sentenced to prison for identity theft. Once this list was generated, researchers used the Federal Bureau of Prisons (BOP) Inmate Locator to determine if the individuals were currently housed in federal facilities. Researchers visited the facilities that housed the largest number of inmates on the list in each of the six regions in the United States as defined by the BOP (Western, North Central, South Central, North Eastern, Mid-Atlantic, and South Eastern) and solicited the inmates housed in these prisons. A total of 14 correctional facilities were visited and 65 individuals incarcerated for identity theft or identity theft related crimes were interviewed between March 2006 and February 2007. Researchers used semi-structured interviews to explore the offenders' decision-making processes. When possible, interviews were audio recorded and then transcribed verbatim. Part 1 (Quantitative Data) includes the demographic variables age, race, gender, number of children, highest level of education, and socioeconomic class while growing up. Other variables include prior arrests or convictions and offense type, prior drug use and if drug use contributed to identity theft, if employment facilitated identity theft, if they went to trial or plead to charges, and sentence length. Part 2 (Qualitative Data), includes demographic questions such as family situation while growing up, highest level of education, marital status, number of children, and employment status while committing identity theft crimes. Subjects were asked about prior criminal activity and drug use. Questions specific to identity theft include the age at which the person became involved in identity theft, how many identities he or she had stolen, if they had worked with other people to steal identities, why they had become involved in identity theft, the skills necessary to steal identities, and the perceived risks involved in identity theft.

Between 1982 and September 2024, 82 out of the 151 mass shootings in the United States were carried out by White shooters. By comparison, the perpetrator was African American in 26 mass shootings, and Latino in 12. When calculated as percentages, this amounts to 54 percent, 17 percent, and eight percent respectively. Race of mass shooters reflects the U.S. population Broadly speaking, the racial distribution of mass shootings mirrors the racial distribution of the U.S. population as a whole. While a superficial comparison of the statistics seems to suggest African American shooters are over-represented and Latino shooters underrepresented, the fact that the shooter’s race is unclear in around nine percent of cases, along with the different time frames over which these statistics are calculated, means no such conclusions should be drawn. Conversely, looking at the mass shootings in the United States by gender clearly demonstrates that the majority of mass shootings are carried out by men. Mass shootings and mental health With no clear patterns between the socio-economic or cultural background of mass shooters, increasing attention has been placed on mental health. Analysis of the factors Americans considered to be to blame for mass shootings showed 80 percent of people felt the inability of the mental health system to recognize those who pose a danger to others was a significant factor. This concern is not without merit – in over half of the mass shootings since 1982, the shooter showed prior signs of mental health issues, suggesting improved mental health services may help deal with this horrific problem. Mass shootings and guns In the wake of multiple mass shootings, critics have sought to look beyond the issues of shooter identification and their influences by focusing on their access to guns. The majority of mass shootings in the U.S. involve firearms which were obtained legally, reflecting the easy ability of Americans to purchase and carry deadly weapons in public. Gun control takes on a particular significance when the uniquely American phenomenon of school shootings is considered. The annual number of incidents involving firearms at K-12 schools in the U.S. was over 100 in each year since 2018. Conversely, similar incidents in other developed countries exceptionally rare, with only five school shootings in G7 countries other than the U.S. between 2009 and 2018.

As of June 2024, there were around 3.09 million ethnic Chinese residents in Singapore. Singapore is a multi-ethnic society, with residents categorized into four main racial groups: Chinese, Malay, Indian, and Others. Each resident is assigned a racial category that follows the paternal side. This categorization would have an impact on both official as well as private matters. Modelling a peaceful, multi-ethnic society The racial categorization used in Singapore stemmed from its colonial past and continues to shape its social policies, from public housing quotas along the ethnic composition in the country to education policies pertaining second language, or ‘mother tongue’, instruction. Despite the emphasis on ethnicity and race, Singapore has managed to maintain a peaceful co-existence among its diverse population. Most Singaporeans across ethnic levels view the level of racial and religious harmony there to be moderately high. The level of acceptance and comfort with having people of other ethnicities in their social lives was also relatively high across the different ethnic groups. Are Singaporeans ready to move away from the CMIO model of ethnic classification? In recent times, however, there has been more open discussion on racism and the relevance of the CMIO (Chinese, Malay, Indian, Others) ethnic model for Singaporean society. The global discourse on racism has brought to attention the latent discrimination felt by the minority ethnic groups in Singapore, such as in the workplace. In 2010, Singapore introduced the option of having a ‘double-barreled’ race classification, reflecting the increasingly diverse and complicated ethnic background of its population. More than a decade later, there have been calls to do away from such racial classifications altogether. However, with social identity and policy deeply entrenched along these lines, it would be a challenge to move beyond race in Singapore.

This Pew Research Center survey asked a nationally representative sample of 7,006 Asian American adults about their experiences living in, and views of, the United States. It covers topics such as racial and ethnic identity, religious identities and practices, policy priorities, discrimination and racism in America, affirmative action, global affairs, living with economic hardship and immigrant experiences.The survey sampled U.S. adults who self-identify as Asian, either alone or in combination with other races or Hispanic ethnicity. It included oversamples of the Chinese, Filipino, Indian, Korean and Vietnamese populations. Respondents were drawn from a national sample of residential mailing addresses, which included addresses from all 50 states and the District of Columbia. Specialized surname list frames were used to supplement the sample. The survey was conducted on paper and web in six languages: Chinese (Simplified and Traditional), English, Hindi, Korean, Tagalog and Vietnamese. Responses were collected from July 5, 2022, to Jan. 27, 2023.

This report includes an identification confidence matrix for all fishes and invertebrates identified from the EBS slope triennial and biennial surveys from 1976 through 2010. The matrix lists a confidence level, with frequency of occurrence and numbers of vouchered lots, for each taxon for each survey year.

Abstract (en): The purpose of this data collection was to provide an appropriate theoretical and empirical approach to concepts, measures, and methods in the study of Black Americans. Developed with input from social scientists, students, and a national advisory panel of Black scholars, the survey investigates neighborhood-community integration, services, crime and community contact, the role of religion and the church, physical and mental health, self-esteem, life satisfaction, employment, the effects of chronic unemployment, the effects of race on the job, interaction with family and friends, racial attitudes, race identity, group stereotypes, and race ideology. Demographic variables include education, marital status, income, employment status, occupation, and political behavior and affiliation. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Black United States citizens 18 years of age or older. National multistage probability sample. The sample is self-weighting. Every Black American household in the continental United States had an equal probability of being selected. Wave 1 was administered to 2,107 respondents, Wave 2 to 951 respondents (including 935 from Wave 1), Wave 3 to 793 respondents (including 779 from Wave 2), and Wave 4 to 659 respondents (including 1 from Wave 1, 28 from Wave 2, and 623 from Wave 3). 1997-11-13 The SAS and SPSS data definition statements have been reissued, and the codebook is being released as a PDF file. PDF questionnaires for Waves 1-4 also have been added to the collection. The Crosswave Variable Listing is now machine-readable and is part of the PDF codebook. Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. National Institute of Mental Health. (1) Data for Wave 1 of this study supersede the data released in NATIONAL SURVEY OF BLACK AMERICANS, 1979-1980 (ICPSR 8512). (2) Users should note that data for the "state and county" codes (Variables 1405, 1407, and 1410) were entered in COUNTY/STATE order and not STATE/COUNTY order, i.e., the first three digits are the county code and the last two digits are the state code. This is the reverse of how Note 3 of the codebook describes the interpretation of these variables. (3) Variables for Wave 2 begin at V3001, Wave 3 begins at V4001, and Wave 4 begins at V5001. (4) The codebook and questionnaires are provided as Portable Document Format (PDF) files. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

This report includes an identification confidence matrix for all fishes and invertebrates identified from the GOA and AI surveys from 1980 through 2011. The matrix lists a confidence level for each taxon by survey year along with its frequency of occurrence on that survey and the number of corroborating voucher collections.

Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2023.

The Justice of Land in a Land of Injustice study was conducted in South Africa. This study examined the lingering effects of Apartheid, with a focus on land distribution. Respondents were asked about their media usage, their interest in politics, whether they discussed politics with others, the general economic situation in South Africa, and their family's standard of living. They were then asked about their relationships with other people, including whether they got along with those with differing opinions, viewpoints, and values. Respondents were also asked about property rights. Questions included whether the land rights of the wealthy should be reduced, if community rights were more important than individual rights, if only property owners should be allowed to vote, if people had a right to land they had lived on for a long time despite not owning it, whether people should receive compensation if their land should be taken away for land reform, the possible consequences of taking away land rights, if land should be taken away from certain groups only, or whether all land right claims should be denied. Respondents were queried about civil rights and freedoms. Questions included how important rights such as free speech, the right to protest, and the right to land ownership were to them. They were also asked whether it was acceptable for the police to search houses without permission in order to fight crime and if sometimes it would be necessary to ignore the law to solve problems. Respondents were then asked to list the groups they do and do not identify with, and how they felt about being a member of a group. They were asked to self-categorize into groups and then queried about their interactions and relations with other groups. They were asked how much contact they had with other groups and how many of their "true" friends were members of different groups. Respondents were also asked how well they understood the customs of other groups, if they were uncomfortable being around or sharing the same political party with a group, and if South Africa would be better off if other groups were not present. Next, respondents were asked about Apartheid. Questions included how many Black people were harmed by Apartheid, if large companies both inside and outside of South Africa were to blame for the harm done, and whether these companies should be forced to pay for the harm they caused under Apartheid. Additionally, they were queried about their life under Apartheid compared to their current life, including past experiences such as having to use a pass to move around, and being assaulted by the police. Respondents were also asked about their knowledge of government organizations including the South African Constitutional Court and Parliament, and their satisfaction with these organizations. They were then asked how important certain issues were to them such as drugs, unemployment, and racial reconciliation. Additionally, they were asked about the election of leaders, and whether multi-party elections were effective ways to choose those leaders. Respondents were also asked about the goods they owned and their financial assets. The survey also included several vignettes with scenarios of land disputes, which were read to the respondents. They were then asked their opinions of the possible outcomes of these vignettes. Demographic information included age, year of birth, highest education level completed, language spoken mostly at home, attendance at places of religious worship, religion, employment status, household composition, how long they have lived in their current community, whether that community had a Traditional Leader, ownership of goods, membership in organizations, whether someone close has died of AIDS, has AIDS, or are HIV positive, and province, size, and metropolitan area of residence. Finally, interviewer attributes and observations are included.

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

Project STRIDE is a three-year research project that examines the effect of stress and minority identity related to sexual orientation, race/ethnicity and gender on mental health. The research describes social stressors that affect minority populations, explores the coping and social support resources that they utilize as they confront these social stressors, and assesses the associations of stress and coping with mental health outcomes including mental disorders and wellbeing. The study also explores the impact of various identity characteristics, such as whether an identity is viewed positively or negatively, or whether it is prominent or not to the relationship of stress and mental health outcomes. The study, using extensive quantitative and some qualitative measures, is a longitudinal survey of 525 men and women between the ages 18 and 59 who are residents of New York City. Socio-demographic information collected about respondents included age, education, race and Hispanic ethnicity, adopting the measures developed and used by the United States Census Bureau in the United States population survey of 2000. In addition to these items, racial/ethnic identity was also assessed with the question "What is the country of origin related to your or your family's ethnic or national background, if any?" Respondents were allowed to select up to two nations from a comprehensive listing. For the purposes of the study, the instrument also assessed whether or not participants were natives of New York City or migrated as adults. Additional demographic variables include employment status, religion, relationship status, and sexual orientation.

In 2021, Canadians who were neither part of a visible minority nor the Indigenous population had an average total income at least 7,600 Canadian dollars higher than these population categories. The visible minorities with the highest average total income that year were people of Chinese, Latin American and Arab origin. Conversely, those with the lowest incomes were other visible minorities and the Native population.

The prevalence of bias in the news media has become a critical issue, affecting public perception on a range of important topics such as political views, health, insurance, resource distributions, religion, race, age, gender, occupation, and climate change. The media has a moral responsibility to ensure accurate information dissemination and to increase awareness about important issues and the potential risks associated with them. This highlights the need for a solution that can help mitigate against the spread of false or misleading information and restore public trust in the media.Data description: This is a dataset for news media bias covering different dimensions of the biases: political, hate speech, political, toxicity, sexism, ageism, gender identity, gender discrimination, race/ethnicity, climate change, occupation, spirituality, which makes it a unique contribution. The dataset used for this project does not contain any personally identifiable information (PII).The data structure is tabulated as follows:Text: The main content.Dimension: Descriptive category of the text.Biased_Words: A compilation of words regarded as biased.Aspect: Specific sub-topic within the main content.Label: Indicates the presence (True) or absence (False) of bias. The label is ternary - highly biased, slightly biased and neutralToxicity: Indicates the presence (True) or absence (False) of bias.Identity_mention: Mention of any identity based on words match.Annotation SchemeThe labels and annotations in the dataset are generated through a system of Active Learning, cycling through:Manual LabelingSemi-Supervised LearningHuman VerificationThe scheme comprises:Bias Label: Specifies the degree of bias (e.g., no bias, mild, or strong).Words/Phrases Level Biases: Pinpoints specific biased terms or phrases.Subjective Bias (Aspect): Highlights biases pertinent to content dimensions.Due to the nuances of semantic match algorithms, certain labels such as 'identity' and 'aspect' may appear distinctively different.List of datasets used : We curated different news categories like Climate crisis news summaries , occupational, spiritual/faith/ general using RSS to capture different dimensions of the news media biases. The annotation is performed using active learning to label the sentence (either neural/ slightly biased/ highly biased) and to pick biased words from the news.We also utilize publicly available data from the following links. Our Attribution to others.MBIC (media bias): Spinde, Timo, Lada Rudnitckaia, Kanishka Sinha, Felix Hamborg, Bela Gipp, and Karsten Donnay. "MBIC--A Media Bias Annotation Dataset Including Annotator Characteristics." arXiv preprint arXiv:2105.11910 (2021). https://zenodo.org/records/4474336Hyperpartisan news: Kiesel, Johannes, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. "Semeval-2019 task 4: Hyperpartisan news detection." In Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 829-839. 2019. https://huggingface.co/datasets/hyperpartisan_news_detectionToxic comment classification: Adams, C.J., Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, Nithum, and Will Cukierski. 2017. "Toxic Comment Classification Challenge." Kaggle. https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge.Jigsaw Unintended Bias: Adams, C.J., Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum. 2019. "Jigsaw Unintended Bias in Toxicity Classification." Kaggle. https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification.Age Bias : Díaz, Mark, Isaac Johnson, Amanda Lazar, Anne Marie Piper, and Darren Gergle. "Addressing age-related bias in sentiment analysis." In Proceedings of the 2018 chi conference on human factors in computing systems, pp. 1-14. 2018. Age Bias Training and Testing Data - Age Bias and Sentiment Analysis Dataverse (harvard.edu)Multi-dimensional news Ukraine: Färber, Michael, Victoria Burkard, Adam Jatowt, and Sora Lim. "A multidimensional dataset based on crowdsourcing for analyzing and detecting news bias." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3007-3014. 2020. https://zenodo.org/records/3885351#.ZF0KoxHMLtVSocial biases: Sap, Maarten, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. "Social bias frames: Reasoning about social and power implications of language." arXiv preprint arXiv:1911.03891 (2019). https://maartensap.com/social-bias-frames/Goal of this dataset :We want to offer open and free access to dataset, ensuring a wide reach to researchers and AI practitioners across the world. The dataset should be user-friendly to use and uploading and accessing data should be straightforward, to facilitate usage.If you use this dataset, please cite us.Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by Shaina Raza, Vector Institute is licensed under CC BY-NC 4.0

The study is a panel survey of adolescents designed to study the complex causal mechanism of structural, social, and cultural integration of adolescents with migration background. The data of three waves are currently available.

The data set of the first wave includes surveys of students, parents, and teachers. It enables studying processes of intergenerational transmission and integration. The survey covers topics of (1) cognitive-cultural integration), (2) structural integration, (3) social integration, (4) emotional-cultural integration, and (5) health and wellbeing. In addition there is (6) detailed information about migration experience and demographics.

Furthermore, the cognitive-cultural integration on the basis of (1) language proficiency tests (measuring linguistic skills) and (2) a cognitive skills test (measurement of intelligence) was measured.

In addition, two aspects of social integration were measured: (1) social integration outside the class context by means of egocentric networks and (2) social integration within the class context by means of a sociometric questionnaire.

The data set of the second wave includes re-interviews with students from the first wave. In addition, in the Netherlands students were interviewed who were not part of the first sample (newcomers). These students were integrated in the school classes between the survey waves. The main questionnaire and the social integration within the class context (sociometric questionnaire) were measured repeatedly.

The data set of the third wave includes re-interviews with students from the first wave or the second wave. Additionally, 10 students are included who were part of the class list of the first wave, and therefore form part of the first wave’s target population, but were absent at the days of the school surveys in wave 1 and wave 2.

The main questionnaire was measured repeatedly.

In addition, two aspects of social integration were measured: (1) social integration outside the class context by means of egocentric networks and (2) social integration within the class context by means of a sociometric questionnaire (only in NL).

The survey instrument includes country-specific variations. The questionnaire also varies between various modules. For more information, see the study documentation.

Cognitive-cultural integration: language (objective measures of proficiency in the host country’s language, self-assessed knowledge of L1, self-assessed knowledge of L2, language use, language spoken at home); measurement of cognitive skills; leisure time activities (memberships, leisure time activities); number of books at home.

Structural Integration: school performance (self-assessment, grades, setting system, school type, repeating classes, private lessons); attitudes towards school (favourite subjects, educational aspirations, self-efficacy, anti-school norms, efforts in school, value of education, status maintenance motive, teacher support, satisfaction with school, success probabilities, perceived association between educational and occupational success, expected discrimination, financial restrictions, educational costs); economic situation (side job, pocket money, possessions, expected development of own economic situation); deviant behaviour and delinquency (school-related problem behaviour, delinquent behaviour).

Social Integration: sociometric information within classrooms; strong ties (ethnic background of friends); contact person in case of problems; person one is having trouble with; weak ties (in school, neighbourhood, clubs/associations); discrimination (victimisation in school, perceived discrimination); attitudes towards other ethnic groups; romantic relationships (characteristics of partner and relationship, expectations about the future of the relationship), family relations (parental support, parent-child contact, family cohesion, parental expectations, family conflict, embeddedness/influence of parents).

Emotional-cultural Integration: identity (with respect to host, respectively sending country, importance of ethnic identity); attitudes towards integration; religion (religious affiliation, importance of religion, religious practices); attitudes and norms (gender roles, violence legitimizing norms of masculinity, tolerance).

Health and well-being: personality and psychological well-being (life satisfaction, self-esteem, behavioural problems, self-control); health (general health status, health problems, sleeping...

The study is a panel survey of adolescents designed to study the complex causal mechanism of structural, social, and cultural integration of adolescents with migration background. The data of three waves are currently available.

The data set of the first wave includes surveys of students and parents. It enables studying processes of intergenerational transmission and integration. The survey covers topics of (1) cognitive-cultural integration), (2) structural integration, (3) social integration, (4) emotional-cultural integration, and (5) health and wellbeing. In addition there is (6) detailed information about migration experience and demographics.

Furthermore, the cognitive-cultural integration on the basis of (1) language proficiency tests (measuring linguistic skills) and (2) a cognitive skills test (measurement of intelligence) was measured.

In addition, two aspects of social integration were measured: (1) social integration outside the class context by means of egocentric networks and (2) social integration within the class context by means of a sociometric questionnaire.

The data set of the second wave includes re-interviews with students from the first wave. In addition, in the Netherlands students were interviewed who were not part of the first sample (newcomers). These students were integrated in the school classes between the survey waves. The main questionnaire and the social integration within the class context (sociometric questionnaire) were measured repeatedly.

The data set of the third wave includes re-interviews with students from the first wave or the second wave. Additionally, 10 students are included who were part of the class list of the first wave, and therefore form part of the first wave’s target population, but were absent at the days of the school surveys in wave 1 and wave 2.

The main questionnaire was measured repeatedly.

In addition, two aspects of social integration were measured: (1) social integration outside the class context by means of egocentric networks and (2) social integration within the class context by means of a sociometric questionnaire (only in NL).

The survey instrument includes country-specific variations. The questionnaire also varies between various modules. For more information, see the study documentation.

Cognitive-cultural integration: language (objective measures of proficiency in the host country’s language, self-assessed knowledge of L1, self-assessed knowledge of L2, language use, language spoken at home); measurement of cognitive skills; leisure time activities (memberships, leisure time activities); number of books at home.

Structural Integration: School performance (self-assessment, grades, setting system, school type, repeating classes, private lessons); attitudes towards school (favourite subjects, educational aspirations, self-efficacy, anti-school norms, efforts in school, value of education, status maintenance motive, teacher support, satisfaction with school, success probabilities, perceived association between educational and occupational success, expected discrimination, financial restrictions, educational costs); economic situation (side job, pocket money, possessions, expected development of own economic situation); deviant behaviour and delinquency (school-related problem behaviour, delinquent behaviour).

Social Integration: Sociometric information within classrooms; strong ties (ethnic background of friends); contact person in case of problems; person one is having trouble with; weak ties (in school, neighbourhood, clubs/associations); discrimination (victimisation in school, perceived discrimination); attitudes towards other ethnic groups; romantic relationships (characteristics of partner and relationship, expectations about the future of the relationship), family relations (parental support, parent-child contact, family cohesion, parental expectations, family conflict, embeddedness/influence of parents).

Emotional-cultural Integration: Identity (with respect to host, respectively sending country, importance of ethnic identity); attitudes towards integration; religion (religious affiliation, importance of religion, religious practices); attitudes and norms (gender roles, violence legitimizing norms of masculinity, tolerance).

Health and well-being: Personality and psychological well-being (life satisfaction, self-esteem, behavioural problems, self-control); health (general health status, health problems, sleeping behaviour, weight, height, health related behaviour, future expectations about health).

Demography and migration history: Gender; age; living situation and household composition (household members, household situation, neighbourhood); social background (parents´ education, parents´ employment status, parents´ occupation), migration history (child´s, parents´, and grandparents´ country of birth, age of migration, home-country visits, return migration).

Demographic characteristics of Canada's military and veteran population: Canada, provinces and territories, census metropolitan areas and census agglomerations with partsFrequency: OccasionalTable: 98-10-0142-01Release date: 2023-11-15Geography: Canada, Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration partUniverse: Population aged 17 and over in private households, 2021 Census — 25% Sample dataVariable List: Visible minority (15), Religion (25), Generation status (4), Age (10B), Gender (3), Statistics (3), Military service status (4A)Footnotes: 1 Religion Religion refers to the person's self-identification as having a connection or affiliation with any religious denomination, group, body, or other religiously defined community or system of belief. Religion is not limited to formal membership in a religious organization or group. For infants or children, religion refers to the specific religious group or denomination in which they are being raised, if any. Persons without a religious connection or affiliation can self-identify as atheist, agnostic or humanist, or can provide another applicable response. 2 Generation status Generation status refers to whether or not the person or the person's parents were born in Canada. 3 Age 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date). 4 Gender Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender. 5 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. 6 Visible minority Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese." 7 Military service status Military service status refers to whether or not the person is currently serving or has previously served in the Canadian military. Military service status is asked of all Canadians aged 17 and older. For the purposes of the 2021 Census, Canadian military service includes service with the Regular Force or Primary Reserve Force as an Officer or Non-Commissioned Member. It does not include service with the Cadets, Cadet Organizations Administration and Training Service (COATS) instructors or the Canadian Rangers. 8 For more information on religion variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Religion Reference Guide, Census of Population, 2021. 9 Visible minority" refers to whether a person is a visible minority or not as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as "persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. In 2021 Census analytical and communications products the term "visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere."10 For more information on visible minority and population group variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Visible Minority and Population Group Reference Guide, Census of Population, 2021. 11 For more information on the military service status variable, including data quality and comparability with other sources of data, please refer to the Canadian Military Experience Reference Guide, Census of Population, 2021. 12 'First generation' includes persons who were born outside Canada. For the most part, these are people who are now, or once were, immigrants to Canada. 13 'Second generation' includes persons who were born in Canada and had at least one parent born outside Canada. For the most part, these are the children of immigrants. 14 'Third generation or more' includes persons who were born in Canada with all parents born in Canada.

In a global survey conducted in 2023, ***** percent of respondents from 30 countries identified themselves as transgender, non-binary/non-conforming/gender-fluid, or in another way. In Switzerland, around *** percent of the respondents stated to identify themselves with one of the listed genders.

This data set contains frequency counts of target words in 175 million academic abstracts published in all fields of knowledge. We quantify the prevalence of words denoting prejudice against ethnicity, gender, sexual orientation, gender identity, minority religious sentiment, age, body weight and disability in SSORC abstracts over the period 1970-2020. We then examine the relationship between the prevalence of such terms in the academic literature and their concomitant prevalence in news media content. We also analyze the temporal dynamics of an additional set of terms associated with social justice discourse in both the scholarly literature and in news media content. A few additional words not denoting prejudice are also available since they are used in the manuscript for illustration purposes. The list of academic abstracts analyzed in this work was taken from the Semantic Scholar Open Research Corpus (SSORC). The corpus contains, as of 2020, over 175 million academic abstracts, and associated metadata, published in all fields of knowledge. The raw data is provided by Semantic Scholar in accessible JSON format. Textual content included in our analysis is circumscribed to the scholarly articles’ titles and abstracts and does not include other article elements such as main body of text or references section. Thus, we use frequency counts derived from academic articles’ titles and abstracts as a proxy for word prevalence in those articles. This proxy was used because the SSORC corpus does not provide the entire text body of the indexed articles. Targeted textual content was located in JSON data and sorted by year to facilitate chronological analysis. Tokens were lowercased prior to estimating frequency counts. Yearly relative frequencies of a target word or n-gram in the SSORC corpus were estimated by dividing the number of occurrences of the target word/n-gram in all scholarly articles within a given year by the total number of all words in all articles of that year. This method of estimating word frequencies accounts for variable volume of total scientific output over time. This approach has been shown before to accurately capture the temporal dynamics of historical events and social trends in news media corpora. It is possible that a small percentage of scholarly articles in the SSORC corpus contain incorrect or missing data. For earlier years in the SSORC corpus, abstract information is sometimes missing and only article’s title information is available. As a result, the total and target word count metrics for a small subset of academic abstracts might not be precise. In a data analysis of 175 million scientific abstracts, manually checking the accuracy of frequency counts for every single academic abstract is unfeasible and hundred percent accuracy at capturing abstracts’ content might be elusive due to a small number of erroneous outlier cases in the raw data. Overall, however, we are confident that our frequency metrics are representative of word prevalence in academic content as illustrated by Figure 2 in the main manuscript, which shows the chronological prevalence in the SSORC corpus of several terms associated with different disciplines of scientific/academic knowledge. Factor analysis of frequency counts time series was carried out only after Bartlett’s test of sphericity and Kaiser-Meyer-Olkin (KMO) test confirmed the suitability of the data for factor analysis. A single factor derived from the frequency counts time series of prejudice-denoting terms was extracted from each corpus (academic abstracts and news media content). The same procedure was applied for the terms denoting social justice discourse. A factor loading cutoff of 0.5 was used to ascribe terms to a factor. Chronbach alphas to determine if the resulting factors appeared coherent were extremely high (>0.95). The textual content of news and opinion articles from the outlets listed in Figure 5 of the main manuscript is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from original sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions. Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1 million words of article content from an outlet. Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in...

"Class of worker by visible minority, selected sociodemographic characteristics and the census year: Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts (1)Frequency: OccasionalTable: 98-10-0645-01Release date: 2024-03-26Geography: Canada, Geographical region of Canada, Province or territory, Census metropolitan area, Census metropolitan area partUniverse: Persons in private households in occupied private dwellings, 2021 and 2016 censuses — 25% Sample dataVariable List: Class of worker (5B), Gender (3a), Age and first official language spoken (10), Immigrant and generation status (9), Visible minority (15), Highest certificate, diploma or degree (6A), Percent, Census year (2)" List of abbreviations and acronyms found within various Census products.(https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm)" Footnotes: 1 Historical comparison of geographic areas The boundaries and names of census geographies can change from one census to the next. In order to facilitate data comparisons between censuses, previous census data have been adjusted to reflect as closely as possible the 2021 boundaries of these areas. The methodology used for this adjustment involved spatially linking blocks of previous censuses (concordance to the 1996 Census used the 1996 enumeration areas to the 2021 boundaries). A previous census block was linked to the 2021 area within which its representative point fell. A limited number of interactive linkages were completed to further enhance the adjustment in certain areas. For some census geographies, it was not possible to reflect the 2021 boundaries. The 2021 boundaries may not be reflected as there was no previous census block to assign to the 2021 area. As well previous census data for some 2021 areas may not be available due to the fact that the concordance did not produce an accurate representation of the 2021 area.2 Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender. 3 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. The sex variable in census years prior to 2021 and the two-category gender variable in the 2021 Census are included together. Although sex and gender refer to two different concepts, the introduction of gender is not expected to have a significant impact on data analysis and historical comparability, given the small size of the transgender and non-binary populations. For additional information on changes of concepts over time, please consult the Age, Sex at Birth and Gender Reference Guide.4 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date). 5 First official language spoken First official language spoken refers to the first official language (English or French) spoken by the person. 6 'Immigrant status' refers to whether the person is a non-immigrant, an immigrant or a non-permanent resident. 'Period of immigration' refers to the period in which the immigrant first obtained landed immigrant or permanent resident status. For more information on immigration variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021. 7 Generation status refers to whether or not the person or the person's parents were born in Canada. 8 "Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. "9 Highest certificate, diploma or degree is the classification used in the census to measure the broader concept of 'Educational attainment.' This variable refers to the highest level of education that a person has successfully completed and is derived from the educational qualifications questions, which asked for all certificates, diplomas and degrees to be reported. The general hierarchy used in deriving this variable (high school, trades, college, university) is loosely tied to the 'in-class' duration of the various types of education. At the detailed level, someone who has completed one type of certificate, diploma or degree will not necessarily have completed the credentials listed below it in the hierarchy. For example, a person with an apprenticeship or trades certificate or diploma may not have completed a high school certificate or diploma, nor does an individual with a 'master's degree' necessarily have a 'university certificate or diploma above bachelor level.' Although the hierarchy may not fit all programs perfectly, it gives a general measure of educational attainment. This variable is reported for persons aged 15 years and over in private households. 10 Class of worker refers to whether a person is an employee or is self-employed. The self-employed include persons with or without a business, as well as unpaid family workers. 11 Includes persons aged 15 years and over who have worked at some point in time during the reference period. In 2021, this period was between January 2020 and May 2021.12 Includes self-employed persons aged 15 years and over with or without an incorporated business and with or without paid help, as well as unpaid family workers.13 Includes self-employed persons whose business is incorporated with or without employees.14 Includes self-employed persons whose business is unincorporated. Also included among the self-employed are unpaid family workers. This category includes persons who work without pay in a business, farm or professional practice owned and operated by another family member living in the same dwelling.15 "Visible minority" refers to whether a person is a visible minority or not as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as "persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. In 2021 Census analytical and communications products the term "visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere."16 For more information on visible minority and population group variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Visible Minority and Population Group Reference Guide, Census of Population, 2021.17 "In 2021 Census analytical and communications products, the term visible minority" has been replaced by the terms "racialized population" or "racialized groups" In 2021 Census analytical and communications products, the term visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere."18 "The abbreviation n.i.e." means "not included elsewhere." This category includes persons who provided responses that are classified as a visible minority but that cannot be classified with a specific visible minority group. Such responses include for example "Guyanese Pacific Islander Polynesian Tibetan" and "West Indian."19 In 2021 Census analytical and communications products, this category is referred to as the rest of the population."

The population of India is divided into several groups based on social, educational, and financial statuses. The formation of these groups is a result of the historical social structure of the country. Between 2019 and 2021, Other Backward Class (OBC) constituted the largest part of Indian households accounting for about ** percent. On the other hand, Schedule Tribes formed about *** percent of households. How prosperous is India’s caste-based society? India suffers from extreme social and economic inequality. The combined share of Schedule Tribe and Schedule Caste in the affluent population of India was less than ** percent. Contrary to this, economically and socially stronger groups constituted the major part of the affluent population. Hence, indicating a strong relationship between caste and prosperity. India’s thoughts on caste-based reservation The constitution of India provides reservations to the weaker sections of the society for their upliftment and growth. However, the need for reservation has increased with time, making the whole situation even more complicated. People are divided over the existence of a system that provides preference to certain castes or sects. In a survey conducted in 2016 about providing employment reservation to young adults of Schedule Caste and Schedule Tribe, many people expressed opposition. More than ** percent of opposition came from upper Hindu caste. Minimum opposition was observed from the people belonging to Schedule Tribe and Schedule Caste.