The quality of preschool education is related to the stability of the early childhood teaching force. With the help of qualitative research methods, the study analyzed the data of eight teachers who left the profession and explored the process of teachers leaving the profession, and found that the encounter between "settling down" and "professional feelings", the struggle for transformation between "professional feelings" and "the situation", and the struggle for transformation between "settling down" and "the situation" are all related to the stability of the early childhood education workforce. It was found that the encounter and tug-of-war between "settling down" and "professional feelings", the struggle for transformation between "professional feelings" and "the situation", and the rational weighing between "settling down" and "the situation" are the important factors affecting the departure from the profession. The essence is the tension between "teachers as human beings" and "human beings as teachers". Therefore, it is necessary to pay attention to the unity of "person" and "teacher", and to alleviate the problem of teachers leaving the organization by creating a fair, democratic and professional working atmosphere and strengthening the awareness of professional education.

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.

This study aims to scrutinize specific long-term sustainability industrial indicators in Thailand as a representative of an emerging economy. The study uses a Bloomberg database comprising all Thai listed companies on the Stock Exchange of Thailand from 2013 to 2023. The research employs a two-step Generalized Method of Moments (GMM) statistics to assess the enduring impact on industrial sustainability. These results provide consistent, significant and positive relationships between asset turnover and sales with all industrial sustainability. The results additionally reveal that some other factors may moderate industrial sustainability but reveal the GDP growth rate and institutional shareholders are less likely to be corporate sustainability to all indicators. The results provide insight into valuable guidance to management teams, financial statements’ users, investors and other stakeholders on designing effective operations and investment strategies to improve sustainability.

This study aims to examine whether banks are compliant with adopting sustainability regulations and guidelines, and how they disclose their sustainable finance activities in sustainability reporting by providing case of Indonesian banking. Previous research provided discussions on the role of governance in supporting many variables as quantitative studies, but failed to demonstrate on going practices of how banking industries implement sustainable finance governance. Hence, this study provides originality by analyzing the extend of disclosures in order to evaluate their commitments in responding to sustainability regulations and guidelines, through disclosures of economic, environment, social, and governance (EESG) information in annual and sustainability reports. The samples were undertaken by examining the contents of sustainability and annual reports published for the financial year 2016 to 30 June 2021, for the Indonesian banks listed in business category 4, business category 3, and international banks, with the total of 202 reports. The results indicate that the implementation of sustainable finance in EESG information increases annually with social performances are the highest information disclosed, while the governance and economic information received the lowest level of disclosure. Results of this study will benefit policymakers, banks, and related companies to understand sustainable finance governance, and reveal the importance the role of banking industries to support Sustainable Development Goals (SDGs). Providing the insights of the ongoing discussions are expected to suggest following actions for further policies to support the implementation of sustainable finance, in particular to establish sustainability governance as a foundation of commitments, beyond complying to regulations.

This study analyzes the importance of strengthening the design of Indonesia’s maritime axis policy. This research uses a qualitative approach to systematically explain the dynamics and importance of strengthening world maritime policy, where the Nvivo 12 Plus tool is used to analyze data and answer the research questions posed. This research shows that Indonesia still has complex bureaucratic and institutional problems and aspects of political identity and leadership attitudes that require systematic and comprehensive improvement. Then, the draft for strengthening the maritime axis policy in Indonesia includes three policy recommendations: reformulating the focus of the maritime axis policy, comprehensive and coherent governance, and an integrated administrative framework, as well as improving the political identity and attitudes of leaders in public policy. Substantially, the relative failure of the Global Maritime Axis (GMA) policy, known as Joko Widodo’s concept of regulating the Indonesian government based on geographical location, was caused by the dominance of political factors and domestic bureaucratic problems. Apart from that, the lack of priority narratives in the maritime and development sectors means that the Indonesian government’s priorities are more oriented towards GMA infrastructure aspects and at the expense of other fundamental elements. This study encourages the Indonesian government to accelerate a more substantive GMA. However, this research needs to be expanded because the analysis results were only carried out through secondary data and focused on two important aspects of GMA. Therefore, further research is needed that explains the prospects for GMA policy in Indonesia in more detail.