Applying critical realism in qualitative research: methodology meets method
Bibliographic record
Abstract
Critical realism (CR) is a useful philosophical framework for social science; however, little guidance is available on which precise methods – including methods of data collection, coding, and analysis – are best suited to applied CR research. This article provides a concrete example of applied qualitative research using CR as a philosophical and methodological framework. Drawing examples from a study of Canadian farm women’s experiences with agricultural policy, I suggest a flexible deductive process of coding and data analysis that is consistent with CR ontology and epistemology. The paper follows the typical stages of qualitative research while demonstrating the application of methods informed by CR at each stage. Important considerations CR ontology and epistemology raise, such as the use of existing theory and critical engagement with participants’ knowledge and experience, are discussed throughout. Ultimately, I identify two key causal mechanisms shaping the lives of farm women and suggest a future direction for feminist political economy theory to more effectively analyze women’s work in agricultural contexts.
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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 | 0.360 | 0.374 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".