Future of Work, Text to Data, Power
I am a researcher and educator in human resources management and labor and employment relations. I study barriers to job mobility, power imbalances in employment relationships, and future of work phenomena (gig work, remote work, outsourcing, offshoring, offshore outsourcing, virtual companies, etc.) and the impact of technology.
I co-developed a method to transform unstructured text to data. Context Rule Assisted Machine Learning (CRAML) gives experts the power to build a ML model and scale any classification scheme over very large document corpora with a desktop.
Check out recent text to data work, the CRAML Github, new research publications, case studies in labor and employment relations, and if you are a scholar interested in creating your own data from text in the employment area, please reach out and consider working with me.