Applying Meta-Theory to Qualitative and Mixed-Methods Research
Bibliographic record
Abstract
Meta-theory refers to broad perspectives, which make claims regarding the nature of reality. Meta-theories philosophically underpin research and practice. Despite this centrality of meta-theory to research and practice, research studies seldom have a strong articulated philosophical basis. There are persuasive philosophical arguments for invoking meta-theory in qualitative and mixed-methods research. We argue that selecting and applying a particular meta-theory is a matter of personal expression and historicity. In this article, we describe the meta-theory of critical realism (CR), which underpins our research around complex heart failure disease management interventions. CR posits that reality is mind independent and views this reality via a stratified ontology. Its explanatory focus, generative logic, multifactorial and open systems approach, and its openness to a variety of methods make it a viable meta-theory for research in a variety of disciplines, utilizing qualitative, quantitative, and mixed methods. CR hermeneutics, ethnographies, grounded theories, mixed-methods studies, and critical realist reviews follow the meta-theoretical assumptions of CR; these are extremely useful in exploring complex interventions holistically, including their components, contexts, and mechanisms.
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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 | 0.490 | 0.596 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.034 | 0.024 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".