Getting Real About Critical Realist Interviewing: Five Principles to Guide Practice
Bibliographic record
Abstract
Critical realism refers to a broad project to realize a post-positivist social science. At its best, it responds to the challenges thrown down by social constructionist critiques of positivist science, while also allowing us to make interrogable claims about reality—a priority for many working to expose and eradicate structural oppression. In the social sciences, interviews remain one of the foremost methods through which researchers generate data to inform our understanding of reality. In this article, however, we argue that for critical realism to deliver on its promise as a philosophy of science for critical social scientists, we need theoretically sound guidance on what a critical realist approach to research interviewing might look like. Currently, this guidance is lacking. Through a systematic analysis of prominent qualitative research interviewing textbooks, we found that critical realism is ignored, mischaracterized, and underdeveloped. In response, we offer five principles, rooted in critical realism’s key tenets, that can guide researchers as they design, conduct, and evaluate critical realist interview studies. These principles are: (1) craft interview protocols to generate data that can inform answers to ontological research questions; (2) keep in view the interview as social practice throughout the study; (3) treat interview data as both interactively achieved co-constructions and as verifiable evidence for real phenomena; (4) be guided and informed by an aim to reduce suffering and promote social justice; and (5) demonstrate reflexivity as ongoing self-awareness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.339 | 0.241 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.020 | 0.104 |
| Scholarly communication | 0.035 | 0.030 |
| Open science | 0.013 | 0.025 |
| Research integrity | 0.022 | 0.043 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".