Alaql Abeer Abdullah, Alqurashi Fahad, Mehmood Rashid
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
High Performance Computing Center, King Abdulaziz University, Jeddah, Saudi Arabia.
Sci Prog. 2023 Oct-Dec;106(4):368504231213788. doi: 10.1177/00368504231213788.
The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958-2022 and for the professionals' perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.
激进的资本主义方式对社会、经济和地球可持续性的影响重大。通货膨胀、能源成本、税收和利率等经济问题持续存在，并且因战争、大流行病和环境灾难等全球事件而进一步加剧。持续不断的金融危机历史暴露了现代经济的弱点。大量人员辞职的“大辞职潮”加剧了人们的担忧。劳动力的多样性带来了新的挑战。变革性方法对于保护社会、经济和地球至关重要。在这项工作中，我们使用大数据和机器学习方法来发现多代劳动力市场的多视角参数。学术视角的参数是通过使用1958年至2022年期间来自科学网的35000篇文章摘要发现的，专业人士视角的参数是通过使用2022年的57000条领英帖子发现的。我们总共发现了28个参数，并将它们分为五个宏观参数，即学习与技能、就业部门、消费行业、学习与就业问题以及特定代际问题。开发了一个完整的机器学习软件工具用于数据驱动的参数发现。应用了各种定量和可视化方法，并提取了多个分类法来探索多代劳动力市场。提供了使用100多篇研究文章对多代劳动力市场的知识结构和文献综述。预计这项工作将加强基于人工智能的知识发现和系统参数发现方法的理论和实践，以开发自主能力和系统，并促进劳动经济学和市场的新方法，从而推动可持续社会和经济的发展。
