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Diversifying the teaching profession has been of international concern for several decades. While most attention has been devoted to issues of ‘race’ and ethnicity, in comparison, social class has been relatively invisible. Research suggests that those from working class backgrounds experience challenges with regard to belonging in what has been regarded as a middle class teaching profession. An area unexplored has been the complexities of researching with student teachers from under-represented groups, including those from working class backgrounds. This chapter draws on research conducted as part of the Access to Post-primary Teaching (APT) project funded under the Higher Education Authority's Programme for Access to Higher Education (PATH): Strand 1 – Equity of Access to Initial Teacher Education. APT supports the participation of student teachers from lower socio-economic groups in initial teacher education. Following the introduction and literature review, we provide information about the methodology of the overall project, as well as the data upon which we draw in this chapter. Next, we present a critical reflective analysis of working with APT participants over the last six years, drawing on our own critical reflections as researchers, as well as the voices of our participants through the project's research strand. Here we highlight concerns pertaining to relative researcher-participant positionality, and issues of identity and disclosure. Finally, we interrogate our analysis using the methodological literature about researching with marginalised groups and end with recommendations for supporting researcher reflexivity.

Co-production has received increasing attention from managers and researchers in public services. In the health care sector, co-production has become a by-word for the meaningful engagement of patients yet there is still a lack of knowledge around what works when co-producing services. The paper sets out a set of pragmatic principles which may guide anyone embarking on co-producing health care services, and provides an illustration of a co-produced Young People’s Health Research Group in England. We conclude by outlining some learning points which are useful when establishing co-production projects.

There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.

In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.

The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.

This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.