Data Critique
Data Critique
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Data Critique on Critical Race Theory
Data Critique on Critical Race Theory
Introducing Our Dataset
Dataset 1: Police Killings by Race (2013-2024)
This data displays the number of racialized police killings in the United States between 2013 and 2024. The title makes reference to race, because it has been shown that African Americans have a higher rate of police killings than do White people. Aside from Native Hawaiians and Pacific Islanders, Black people, American Indians, Hispanic people, White people, and Asian people are also included in the dataset. This dataset provides an extensive perspective on the prejudice of various racial groupings, which is necessary to guarantee accuracy in statistical research about race-based police shootings.
Dataset 2: Racial Distribution of Arrests in the U.S.
This dataset highlights differences between various racial groupings by examining the racial distribution of arrests in the United States. This data demonstrates the gap between the races, pointing to variations in racial arrest trends. The percentage of general arrests made among the population classified by race/ethnicity is fully disclosed in this database. This indicates that the information enables the detection of potential biases in law enforcement operations and provides insight into the impact of race on arrest rates.
Information, Events, or Phenomena the Datasets Can Illuminate
Both data sets play a significant role in understanding the influence of race on criminal justice experiences. If we take a critical stand point, therefore, it is evident that these data sets show how racial minorities face systemic biases and structural inequalities.
Police Killings by Race:
Police killings data set explains how lethal force is used against blacks by law enforcement in different situations. Black, Native Hawaiian, and Pacific Islander people are more likely to be killed by the police because of societal standards, organizational policies and racial prejudice as explicated in Critical race theory. It means that this dataset calls for urgent police reform and accountability measures to address these disparities. This shows that there is a systemic problem within policing practices causing the disparate impact on racial minorities increasing their odds of fatal encounters with the police.
Racial Distribution of Total Arrests by Race:
The data from the arrests can help understanding about how the different races experience arrest under criminal justice system’s custody. The data discloses serious disparities between ethnic groups and reveals that blacks and native Americans are arrested over-representatively compared to other races. These disparities emanate from structural biases and discriminatory practices within law enforcement agencies and courts as discussed by critical race theory. A wider picture of these differences is shown through this dataset which includes but not limited to over-policing in communities of color, profiling based on race or ethnicity, as well as long-term social-economic consequences for such communities.
Overall Insights:
When combined, these databases reveal the wide spectrum of racial disparities that exist in the entire criminal justice system. They indicate the beginnings of this gap as racial profiling by law enforcers and discrimination in courts that cuts across each stage of court processes affecting results for minority races. These revelations are important in informing future policies and interventions geared towards achieving fairness in judicial treatment with respect to color, and it is pertinent to design such interventions to enhance fairness based on racial standpoint.
Limitations of the Data
Quantitative Focus:
The datasets are primarily quantitative and lack qualitative data that could provide deeper insights into personal experiences and the nuanced realities behind the numbers. Qualitative data, such as interviews or case studies, could enrich the understanding of these issues.
Intersectionality:
Datasets about race and other elements of identity such as gender, age, have not been given room to account. Intersectional analysis is necessary to grasp fully the complications inherent in racial inequality in the criminal justice system.
Temporal Limitations:
The data only represent specific time periods and may therefore not reflect long-term trends or changes over time. Longitudinal data would provide a better understanding of how these issues evolve and the effectiveness of interventions over time.
Personal Assumptions and Biases:
One could be led to think that focusing on critical race theory entails examining racial disparities and systemic biases against racial minorities. However, there are many other contributing factors that make it possible for this to happen; they include but are not limited to socio-economic statuses, geographical spaces where they reside and how institutions operate.
Data and Resources Advantages
Police Killings Dataset: This dataset is valuable for highlighting racial disparities in police use of force, offering a clear, quantifiable measure of the impact on different racial groups. It provides a strong foundation for advocating for policy changes and reforms.
Arrests Distribution Dataset: This dataset is essential for understanding racial disparities in the criminal justice system’s arrest practices. It provides insights into the systemic biases that lead to disproportionate arrest rates for certain racial groups, helping to inform efforts to promote fairer law enforcement practices.
Data Critique on Feminist Theory
Data Critique on Feminist Theory
Information Included in the Datasets
Dataset 1: National Crime Victimization Survey (NCVS) The first dataset from the National Crime Victimization Survey (NCVS) shows the rate of violent victimization by genders and type of crime from 2017 to 2020. It offers data on various types of violent crimes such as sexual assault, robbery, aggravated assault, and simple assault. The data is divided according to genders, especially lesbian/gay, bisexual, and straight individuals. It also points out different characteristics of different kinds of violent crime, such as domestic violence, intimate partner violence, stranger violence, and crimes involving injury or weapons.
Dataset 2: Does Victim Gender Matter for Justice Delivery? Police and Judicial Responses to Women’s Cases in India The second dataset is from the study by Nirvikar Jassal, surveying the full responses of accessing justice in India, focusing on the police and judicial system towards women's cases. It gives data from around half a million crime reports combined with court files, documenting delays and dismissals. It also provides us with insights into how gender influences the likelihood of those kinds of responses.
Information, Events, or Phenomena the Datasets Can Illuminate
Both datasets help us analyzing how gender impacts experience with criminal justice system. From a feminist theory perspective, these datasets really illustrate the systemic biases and structural inequalities that women face.
Victimization Rates by Gender and Sexual Orientation: The NCVS data help understand how gender and sexual orientation intersect to influence the likelihood of victimization. Feminist theory examines how societal norms and gender roles contribute to the higher rates of victimization among bisexual individuals and vulnerabilities.
Justice Delivery in India: Jassal's dataset provides different pathways for how women's cases are processed through the Indian criminal justice system. Feminist analysis explores how gender biases impact the likelihood of a woman's case being delayed, dismissed, or resulting in an acquittal. It shows discriminations women face, pointing out larger societal trends to undermine women's experiences and credibility in the criminal justice system.
Limitations of the Data
Quantitative Focus and Lack of Qualitative Insights Both datasets offer quantitative data, which lacks the qualitative depth necessary to fully understand the lived experiences of victims. Feminist theory emphasizes the importance of personal narratives and lived experiences, which are not captured in these datasets. These kinds of inherent impact factors are really important for additional analysis.
Omission of Socio-Economic Factors and Intersectionality The datasets do not consider socio-economic status. Feminist theory stresses the importance of intersectionality, recognizing how overlapping identities shape one's experiences. The lack of this factor limits the ability to comprehensively understand the complexities of gender-based disparities.
Binary Gender Categories Both datasets use binary gender categories, failing to capture the experiences of non-binary or gender-nonconforming individuals. Feminist theory advocates for recognizing the fluidity of gender and the diverse experiences of all gender identities, which are not reflected in these datasets.
Personal Assumptions and Biases
Since we mainly focus feminist theory while facing these two datasets, we tend to view the data through the pathways of disparities between men and women, and we might focus on only those findings that correspond with this perspective. For instance, we may interpret some of quantitative findings in a way that puts the blame on systemic bias against women.But in fact other biases or systemic factors may also play a role here. We may also be more attentive to statistical robustness, which can help in interpretation, compared with other aspects that we have not had much formal training in, such as qualitative richness, an important aspect of a feminist critique.
Data and Resources Advantages
NCVS Dataset The NCVS data is collected through interviews with victims. This method ensures a broad representation of victimization experiences but might be limited by underreporting and the exclusion of unreported crimes. Also because of the potential pressure given by the interviewer, the information may not that be accurate.
Jassal's Dataset Jassal's dataset is an original compilation of police and court records in India, combined to track the process of cases from filing to judicial verdict. The study employs advanced statistical and machine learning techniques to analyze the data, offering robust insights into the justice delivery process. However, the reliance on administrative records means that the data might be subject to reporting biases and omissions. Bt they are still data from governmental department so the reliability should be ensured despite some actions done by the government agents.
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