Economic issues, such as inflation, energy costs, taxes, and interest rates, are a constant presence in our daily lives and have been exacerbated by global events such as pandemics, environmental disasters, and wars. A sustained history of financial crises reveals significant weaknesses and vulnerabilities in the foundations of modern economies. Another significant issue currently is people quitting their jobs in large numbers. Moreover, many organizations have a diverse workforce comprising multiple generations posing new challenges. Transformative approaches in economics and labour markets are needed to protect our societies, economies, and 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 categorised them into 5 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 visualisation 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 AI-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.
翻译:诸如通货膨胀、能源成本、税收和利率等经济问题,是我们日常生活中的常年存在问题,并且由于流行病、环境灾难和战争等全球事件而加剧。金融危机的持续历史揭示了现代经济基础中的严重弱点和脆弱性。目前另一个重要问题是人们大量退出工作。此外,许多组织拥有由多代人组成的多样化劳动力队伍,构成新的挑战。经济和劳动力市场需要改革方法来保护我们的社会、经济和地球。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间劳动力市场的多视角参数。学术视角的参数是通过1958-2022年期间科学网的35 000条文章摘要和专业人员的视角而发现的。2022年以来,我们发现共有28个参数并将其分为5个宏观参数、学习和技能、就业部门、消费者产业、学习和就业问题和代际具体问题。为数据驱动参数的发现开发开发了完整的机器学习软件工具。为数据驱动的参数发现、1958-2022年期间科学网络的35条文章摘要以及专业人员的视角。我们发现总共28个参数,并将它们分为5个宏观参数、学习和技术、学习和就业问题等特定系统,为数据驱动的代内化研究市场提供多种研究方法。利用数字和代内研究方法,为研究市场提供数字和代内研算和代内研研研研研研研研研研研研研研的多种研究方法。