The issue of policy changes to support teacher professional development is an important factor shaping the career trajectory, efficacy, and ultimately the success of Junior Reserve Officer Training Corps (JROTC) instructors and the performance of the secondary students they serve and whose lives they affect. Although a rich body of research associated with policies regarding teacher preparation and professional development exists, a more closely related area of research focused specifically on the policies regarding preparation and professional development of JROTC instructors is limited. This lack of research presents a unique opportunity to explore the experiences of JROTC instructors and their perspectives on policies affecting teacher preparation and professional development. This qualitative exploratory single-case study can help to advance understanding of the complexities and nuances of teacher preparation and professional development policies supporting the JROTC instructors serving in high schools across the United States and overseas. One-on-one interviews with 14 JROTC personnel who had completed required teacher preparation requirements and professional development initiatives were conducted. Data analysis revealed 11 themes. Recommendations for improving policies concerning JROTC instructor preparation and professional development, including placing greater emphasis on the unique requirements, as well as suggestions for future research, are provided.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

Educational quality policies are a basic principle that every Peruvian university educational institution pursues in accordance with Law No. 30220, with the objective of training highly competent professionals who contribute to the development of the country. This study to analyzes educational quality policies with the student’s satisfaction of public and private universities in Peru, according to social variables. The study was descriptive-comparative, quantitative, non-experimental, and cross-sectional. One thousand (1000) students from two Peruvian universities, one public (n = 500) and one private (n = 500), were purposively selected by quota using the SERVQUALing instrument. The findings indicate a moderate level of satisfaction reported by 49.2% of participants, with a notable tendency towards high satisfaction observed in 40.9% of respondents. These results suggest that most students perceive that the actual state of service quality policies are in a developmental stage. The results, therefore, indicate that regulatory measures, including university laws, licensing, and accreditation, significantly influence outcomes. These measures are essential for the effective functioning of universities. In addition, the analysis revealed that female and male students at private universities showed higher levels of satisfaction with the educational services offered. It is concluded that educational quality policies in Peru are still being executed, because the implementation of the University Law is in process, according to the satisfaction of the student, this must be improved in central aspects such as optimizing human resources, infrastructure, equipment, curricular plans that differ from the public to the private university, In addition, this should lead to improving and redefining current policies on educational quality and the economic policies that finance the educational service.

Ignorance of laws and policies creates barriers to the social inclusion of persons with disabilities (PWDs), hindering their full participation in communal life and opportunities. The current study aims to analyze the social inclusion of PWDs in the context of ignorance of laws and policies and how it influences their overall social inclusion. To achieve the study objectives, data were collected from a sample of 488 PWDs, comprising 284 males and 204 females, in the selected six Union Councils (sub-administrative units) of District Malakand, Pakistan. Respondents were chosen through multistage stratified random sampling. In the univariate and multivariate level analyses, the chi-square test and Kendall’s Tau-b test statistics were used to test the relationship between ignorance of laws and policies and the social inclusion of PWDs. Gender and level of disability were used as control variables at the multivariate level. The results of Kendal Tb and chi-square significance values depicted a spurious relation among ignorance of laws and policies and social inclusion of PWDs while controlling respondent’s gender. The results highlighted that ignorance of laws and policies reduced social inclusion in male to a higher extent than female. Additionally, the social inclusion of PWDs with moderate disabilities is more significantly hampered by ignorance of laws and polices than those with severe disabilities.

The objectives of the study are to assess the impact of green human resources management (GHRM) policies and knowledge on the environmental performance of a public transportation company employees. Data from 1130 respondents were analyzed using SmartPLS modeling. The findings that GRHM affected employees of a public transportation company mediated by roles of green human resources management policies and knowledge. GRHM affected public transportation employees’ environmental performance significantly. Employees in the public transportation industry can use the study’s results to their advantage by developing plans to increase their sense of belonging to the company and their impact on the environment. Therefore, many companies understand the value of public transportation employees as the forefront ‘agent of change’ towards a significant positive environmental change in the community.

This study examines the effectiveness of Kazakhstan’s grant funding system in supporting research institutions and universities, focusing on the relationship between funding levels, expert evaluations, and research outputs. We analyzed 317 projects awarded grants in 2021, using parametric methods to assess publication outcomes in Scopus and Web of Science databases. Descriptive statistics for 1606 grants awarded between 2021 and 2023 provide additional insights into the broader funding landscape. The results highlight key correlations between funding, evaluation scores, and journal publication percentiles, with a notable negative correlation observed between international and national expert evaluations in specific scientific fields. A productivity analysis at the organizational level was conducted using non-parametric methods to evaluate institutional efficiency in converting funding into research output. Data were manually collected from the National Center of Science and Technology Evaluation and supplemented with publication data from Scopus and Web of Science, using unique grant numbers and principal investigators’ profiles. This comprehensive analysis contributes to the development of an analytical framework for improving research funding policies in Kazakhstan.