Rural households’livelihood responses to industry-based poverty alleviation as a sustainable route out of poverty

Industrialization is one way to achieve a sustainable route out of poverty.During the implementation of industry-based poverty alleviation projects,rural households’livelihood responses to change are crucial.A stronger livelihood response is conducive to multidimensional poverty relief due to industry-based poverty alleviation projects.Effective poverty alleviation can also stimulate stronger household responses.There is a positive cycle between livelihood response and multidimensional poverty relief effects that can help achieve sustainable poverty alleviation goals.Using a synergistic perspective on the relationship between“people–industry–land”,this paper explains the poverty alleviation logic connecting livelihood response,multidimensional poverty relief,and sustainable routes out of poverty by constructing a four-dimensional livelihood response measurement system with three elements of intensity.We analyzed survey data collected from 2363 households from 4 sample counties in 4 contiguous poverty-stricken areas,and measured and compared the characteristics of rural households’livelihood responses and the factors influencing poverty alleviation projects.Rural households’livelihood responses in four sample counties were moderate.The four dimensions of responses were ranked as livelihood strategy response,livelihood space response,livelihood output response,and livelihood capital response.The three intensities indicated that the perception and willingness elements of livelihood response were very similar,but there was a big gap between those elements and livelihood response actions.At the group level,poor households had higher and more consistent livelihood response than non-poor households.External environment factors(such as location,industry type,village organizational ability,and village atmosphere)and internal family factors(such as resource endowment,income sources,health,education,labor quantity,policy trust,credit availability,and social networks)had a significant impact on households’livelihood response.However,this impact varied across different dimensions and had different intensities.This paper proposes a multidimensional poverty relief mechanism and suggests sustainable routes out of poverty.

Industry-Based Congruence Of Undergraduate Colleges And Universities In Weifang,China

This study investigates the alignment between undergraduate programs in Weifang City and the requirements of the industry,along with key competencies valued by employers.Utilizing a quantitative descriptive research design,data was collected from 370 participants,including faculty members,industry professionals,administrative staff,employers,students,and alumni.The findings reveal a positive perception of current undergraduate programs,highlighting their relevance,practical skills development,engagement of faculty with the industry,and fruitful collaborations with various sectors.Employers exhibited moderate consideration for technical proficiency,soft skills,industry-relevant experience,and professional attitude when hiring graduates.Higher education institutions displayed moderate efforts in enhancing industry-based congruence,particularly in curriculum adaptation,practical learning opportunities,and career services.Various factors,including industry input,labor market analysis,collaboration with professional associations,and economic strategies,were found to moderately influence the alignment between education and industry needs.Despite the positive aspects,the study identified gaps and challenges,such as curriculum disparities and limited collaboration,emphasizing the need for strategic interventions.Policy recommendations were proposed to enhance alignment,including fostering strong university-industry collaborations,mandating internships,faculty development,and promoting student-industry awareness.In conclusion,this study provides valuable insights for educational institutions,industry partners,policymakers,students,and future researchers,aiming to improve the congruence between undergraduate education and industry requirements in Weifang City and beyond.

Researching the Research: Applying Machine Learning Techniques to Dissertation Classification

This research examines industry-based dissertation research in a doctoralcomputing program through the lens of machine learning algorithms todetermine if natural language processing-based categorization on abstractsalone is adequate for classification. This research categorizes dissertationby both their abstracts and by their full-text using the GraphLabCreate library from Apple’s Turi to identify if abstract analysis is anadequate measure of content categorization, which we found was not. Wealso compare the dissertation categorizations using IBM’s Watson Discoverydeep machine learning tool. Our research provides perspectiveson the practicality of the manual classification of technical documents;and, it provides insights into the: (1) categories of academic work createdby experienced fulltime working professionals in a Computing doctoralprogram, (2) viability and performance of automated categorization of theabstract analysis against the fulltext dissertation analysis, and (3) natuallanguage processing versus human manual text classification abstraction.