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This paper aims to provide a historical overview of AA, its purpose and benefits, the legal rationale for the SCOTUS ruling and what it means for colleges and the workplace regarding equitable opportunities for minority groups (which include women, Blacks, Hispanics, Asians and other low-income populations), as they aim for the “American dream”.

SCOTUS decision and rationale, along with literature.

The race-based affirmative action (AA) precedent was recently overturned by the Supreme Court of the United States (SCOTUS) in the case of Students for Fair Admission (SFFA), Inc. vs President and Fellows of Harvard College/University of North Carolina. SCOTUS ruled that race cannot be a specific basis for college admission. In other words, public and private colleges and universities will no longer be able to consider “race” as a factor in deciding which qualified applicants should be admitted to enhance the diversity of their student body.

This is an original analysis.

The purpose of this paper is to understand demographic group (race, first-generation college graduate, gender, age) differences among perceived family and faculty social and family financial support within the US graduate school admissions pipeline in the social sciences.

Using data from a cross-sectional convenience sample survey (N = 99), this paper looks at ordinal social support variables (faculty member support, family social support and family financial support) by demographic groups. This paper uses a Mann–Whitney U test to compare first-generation status, race and gender and a Kruskal–Wallis H test to compare age groups.

This paper finds that applicants over 27 years old had significantly less faculty support in the graduate admissions pipeline compared to other age groups; differences in faculty support across race were marginally significant (p = 0.057). Regarding family social support, this paper finds first-generation applicants, male applicants and applicants over 27 years old report lower levels of support. Finally, this paper finds first-generation applicants and applicants over 27 years old report lower levels of familial financial support.

Previous literature on graduate admissions – published in this journal (Pieper and Krsmanovic, 2022) and others – does not consider experiences up to and before applicants hit the “submit” button on graduate applicants, which the authors term the graduate admissions pipeline. Instead, most previous literatures focus on faculty committees and validity of required application materials. Thus, this study begins to answer Posselt and Grodsky’s (2017) call to develop an understanding of applicant experiences and support within the graduate admissions pipeline.

The purpose of this study is to implement lean six sigma (LSS) methodology to improve the admission process in a higher education institute (HEI).

In this study, case study research methodology is adopted and implemented through an LSS define-measure-analyze-improve-control (DMAIC) framework.

The preliminary investigation showed that the completion of the whole admission process of a new student takes an average of 88 min, which is equivalent to a sigma level of about 0.71 based on the targeted admission cycle time of 60 min. The implementation of the proposed LSS approach increased the sigma level from 0.71 to 2.57, which indicates a reduction in the mean admission cycle time by around 55%. This substantial improvement is expected not only to provide an efficient admission process but also to enhance the satisfaction of students and employees and increase the reputation of the HEI to a significant level.

In this study, the sample size used in the analysis is considered small. In addition, the effectiveness of the proposed approach is investigated using a discrete event simulation with a single-case study, which may limit generalization of the results. However, this study can provide useful guidance for further research for the generalization of the results to wider scopes in terms of different sectors of HEIs and geographical locations.

This study uses several statistical process control tools and techniques through a LSS DMAIC framework to identify and element the root causes of the long admission cycle time at a HEI. The approach followed, and the lessons learned, as documented in the study, can be of a great benefit in improving different sectors of HEIs.

This study is one of the few attempts to implement LSS in HEIs to improve the administrative process so that better-quality services can be provided to customers, such as students and guardians. The project is implemented by a group of undergraduate students as a part of their senior design project, which paves the way for involving students in future LSS projects in HEIs. This study is expected to help to improve understanding of how LSS methodology can be implemented in solving quality-related problems in HEIs and to offer valuable insights for both academics and practitioners.

The purpose of this study is to examine whether implicit bias exists within the graduate admissions process at a large public research university in the Southeast United States. Additionally, this research sought to identify the type of strategies graduate faculty in the USA use to assess their implicit bias and the support they may need to better recognize and gauge implicit bias during the graduate application review process.

This study used the use of a qualitative, phenomenological research design by conducting individual interviews with graduate faculty members that serve on admissions committees.

The findings revealed six themes in relation to the purpose of the study – bias recognition, faculty perceptions of their own bias, faculty perceptions on the bias of others, strategies for the application review process, admission committee safeguards and the need for implicit bias training.

The study outcomes are discussed in relation to the prior research and literature on this phenomenon. Additionally, the study presents research and practical implications, including actionable strategies for how its results can be practically applied.

The uncertainty of getting admission into universities/institutions is one of the global problems in an academic environment. The students are having good marks with highest credential, but they are not sure about getting their admission into universities/institutions. In this research study, the researcher builds a predictive model using Naïve Bayes classifiers – machine learning algorithm to extract and analyze hidden pattern in students’ academic records and their credentials. The main purpose of this research study is to reduce the uncertainty for getting admission into universities/institutions based on their previous credentials and some other essential parameters.

This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) to predict the student’s admission into universities or any higher institutions. The researcher collected data from the Kaggle data sets based on grade point average (GPA), graduate record examinations (GRE) and RANK of universities which are essential to take admission in higher education.

The classification model is built on the training data set of students’ examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictive accuracy rate 72% and has been experimentally verified. To improve the quality of accuracy, the researcher used the Shapiro–Walk Normality Test and Gaussian distribution on large data sets.

The limitation of this research study is that the developed predictive model is not applicable for getting admission into all courses. The researcher used the limited data attributes such as GRE, GPA and RANK which does not define the admission into all possible courses. It is stated that it is applicable only for student’s admission into universities/institutions, and the researcher used only three attributes of admission parameters, namely, GRE, GPA and RANK.

The researcher used the Naïve Bayes classifiers and KDE machine learning algorithms to develop a predictive model which is more reliable and efficient to classify the admission category (Admitted/Not Admitted) into universities/institutions. During the research study, the researcher found that accuracy performance of the predictive Model 1 and that of predictive Model 2 are very close to each other, with predictive Model 1 having truly predictive and falsely predictive rate of 70.46% and 29.53%, respectively.

Yes, it is having a significant contribution for society; students and parents can get prior information about the possibilities of admission in higher academic institutions and universities.

The classification model can reduce the admission uncertainty and enhance the university’s decision-making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to the student’s admission into universities or any higher academic institutions, and it demonstrates many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention.

Although there has been considerable growth in the higher education systems of Turkey and China in about the last two decades, there is still a room for development in enabling equity in all regions, increasing opportunities and resources regardless of socio-economic status or gender differences. Students coming from disadvantaged backgrounds do not have enough tools to change their fate for the better due to the accumulated barriers they face. Given this background, the chapter discusses how the barriers to equitable HE admissions relate to each other and which one has a greater negative impact over the Accumulated Conversion Barriers Modal we propose defined by personal, discriminatory, institutional, and geographical barriers. The perspectives of Turkish and Chinese HE stakeholders were examined through 21 in-depth interviews that were subjected to content analysis and interpreted in a comparative style using the lens of Capabilities Approach of Sen. We also offer policy suggestions to increase students’ conversion capacities for better outcomes to serve both the national and the international educational contexts owing to the adaptable nature of our modal to other countries experiencing similar issues in their higher education systems.

The purpose of this clinical audit was to review the adherence to the 2017 care and treatment review policy across two NHS assessment and treatment units. Care and treatment reviews should be offered to people with an intellectual disability and/or autism who are at risk of admission into a mental health hospital.

Admission and discharge data was collected across two assessment and treatment units between January 2019 and December 2022. Adherence to the care and treatment review policy was also reviewed as was length of inpatient stay. A retrospective evaluation was conducted. Triangulation of data was collected via the trusts’ electronic patient record system and NHS analytics team. Descriptive statistics, Mann–Whitney U test and a one-way ANOVA with post hoc tests were used in the analysis.

An increase in behaviours of challenge and deterioration of mental health were the main reasons for admission. Forty-nine percent (30) of those admitted to the assessment and treatment units accessed a care and treatment review. Care and treatment reviews were more frequently provided for individuals experiencing longer inpatient stays.

There is limited evidence relating to outcomes and impact of care and treatment reviews. Further research is required to explore effectiveness of care and treatment reviews to understand benefits and appropriately prioritise resource.

Education can be classified into formal and informal sectors—the first category as a regular schooling system and the latter category as private tutoring. After completing secondary education, students in many countries receive education from private tutoring to get admission into the university. This study examines the effect of private tutoring on university admission and subsequent students' academic achievement at the university level.

Using survey data from Bangladesh as a case study, this study employs a two-stage least squares (2SLS) methodology.

Considering that coaching centers offer services such as private tutoring, this study finds that an informal education for admission greatly helps academic achievement. Students who benefit from informal schooling are more likely to achieve higher grades in subsequent programs.

This study strongly suggests that formal education at the secondary school level is unable to meet the academic expectations that are demanded at the tertiary level. This forces the development of private tutoring, which supports the students from more financially well-off families to perform well at the cost of educational disparity.

In the aftermath of the Supreme Court’s 2023 decision to effectively end race-conscious admissions practices across the nation, this paper highlights the law’s commitment to whiteness and antiblackness, invites us to mourn and to connect to possibility.

Drawing from the theoretical contributions of Cheryl Harris, Jarvis Givens and Chezare Warren, as well as the wisdom of Justice Ketanji Brown Jackson’s dissenting opinion, this paper utilizes CRT composite counterstory methodology to illuminate the antiblack reality of facially “race-neutral” admissions.

By manifesting the impossible situation that SFFA and the Supreme Court’s majority seek to normalize, the composite counterstory illuminates how Justice Jackson’s hypothetical enacts a fugitive pedagogy within a dominant legal system committed to whiteness as property; invites us to mourn, to connect to possibility and to remain committed to freedom as an intergenerational project that is inherently humanizing.

In a sobering moment where we face the end of race-conscious admissions, this paper uniquely grapples with the contradictions of affirmative action as minimally effective while also radically disruptive.

This study focuses on ratcheting and budget behavior in nonprofit museums. Specifically, the authors examine how performance compared with the budget affects future revenue budgets, and how this differs from the extant literature focused on for-profit organizations. The study focuses specifically on the relationship between museums and their sources of public funding and how this affects how museums prepare budgets.

Based on four years of data covering 97 state-subsidized Danish museums, the authors analyze budget ratcheting using least absolute deviation (LAD) estimations in the form of median regressions.

The authors find that when actual revenue from admission charges is below the budget, the decrease in the following year's budget is greater than the increase in the following year's budget when actual revenue from admission charges is above the budget (i.e. the authors find asymmetrical ratcheting).

The findings are based on a specific setting (Danish museums), and the results may not be generalizable to other settings.

This study provides insights into the museum sector and other sectors with similar characteristics and contributes to understanding the differences between museums and for-profit organizations when it comes to budgeting. As private-sector management practices are gaining ground in the museum sector, it is important to learn more about budgeting-related issues in this sector.

The asymmetrical ratcheting the authors find is the opposite of ratcheting typically found in for-profit organizations. The authors attribute the results to the incentive conflict between museums and their public funding sources. The authors point to the museums' dependence on public funding as an explanation for the results and, thereby, extend the knowledge on ratcheting to organizations with different characteristics than traditional, for-profit organizations.