Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.

The tourism sector in the Aseer region of Saudi Arabia is experiencing significant growth and development, aligning with the country's Vision 2030 strategic framework. However, rapid growth can lead to strategic drift if not managed with vigilance. This study aims to examine the role of strategic vigilance in reducing strategic drift in the tourism sector. The study employs a quantitative approach, utilizing a questionnaire distributed to a sample of 220 staff and directors from the tourism sector. The questionnaire measures the level of strategic vigilance and the level of strategic drift. The study hypothesizes a statistically significant positive relationship between strategic vigilance and reducing strategic drift. Data analysis involves exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The findings are expected to provide insights into the effectiveness of strategic vigilance in mitigating strategic drift and offer recommendations for enhancing the tourism sector's resilience and adaptability to accelerated environmental changes.

This study explored the relationship between Chinese graduate students’ English language proficiency (ELP) and intercultural communicative competence (ICC). With the acceleration of globalization, an increasing number of Chinese students choose to study abroad, making it crucial to enhance their intercultural communication ability and language skills. However, China’s exam-oriented education system to some extent limits students’ holistic development and poses challenges for them in intercultural exchange. A quantitative survey method was employed, collecting questionnaire data from 249 Chinese English-major graduate students to analyze the relationship between their English ability and intercultural competence. The results indicated a certain positive correlation between English proficiency and intercultural competence but also pointed to the need for further unpacking of complexity and influencing factors. Future research with more robust methodology is still warranted to provide deeper insights into the linkage between the two constructs in the Chinese graduate context.

Objective: This research analyzed the psychometric properties of the Ambivalent Classism Inventory (ICA) in Peru. Methodology: A critical review of literature related to poverty, inequality, and structural gaps was conducted, involving 882 participants aged 14 to 89 years (M = 24.61, SD = 9.07). Results: Exploratory-confirmatory factor analyses were satisfactory, finding a similar factorial structure to the original scale and the adaptation (hostile classism, protective paternalism, and complementary class differentiation). Regarding items, there was a reduction, leaving only 12; however, comparing alternative models, the three-factor structure with 12 reagents showed adequate fit (χ² = 214.588, df = 51, p < 0.001; CFI = 0.996; RMSEA = 0.060; SRMR = 0.033), allowing for invariance testing. Practical Implications: The scale allows for investigating attitude profiles of individuals with privileged social class. Contribution: The instrument is a valuable contribution, considering that the nation has a high poverty rate, leading to economic, political, and social inequality among the population.

Papua, one of the provinces in Indonesia, is recognized for its limited infrastructure and high poverty rates. This limitation undoubtedly emphasizes the government’s special attention toward augmenting foreign and domestic investments by expanding industrial sectors to absorb more labor, thereby aiming to enhance the region’s economic performance. The focus of the study seeks to assess the extent to which foreign and domestic investments, industrial employment, and the proliferation of industries in Papua contribute to increasing the Gross Development Product (GDP) and reducing poverty. By employing secondary data from 2016 to 2022 and utilizing the Regression Data Panel method, it encompasses 29 districts. The findings reveal that domestic investment, employment in the industrial sector, and the number of industries significantly influence poverty rates. However, as conclusion, foreign investment, surprisingly, demonstrates no substantial impact on economic performance. This unexpected result might be attributed to issues linked with the inadequate quality of financial performance, which doesn’t align with the available investment funds. Utilizing the analytical network process (ANP), the study outlines two primary strategies. The first involves prioritizing investment expansion by focusing on both domestic and foreign investments. The second strategy emphasizes industrial revitalization through augmenting the number of industries and enhancing labor participation in the industrial sector.