Strange Bedfellows: Exploring Methodological Intersections Between Realist Inquiry and Structural Equation Modeling
Bibliographic record
Abstract
Realist inquiry, based on the philosophy of critical realism, focuses on exploring the underlying mechanisms that drive social phenomena. Structural equation modeling is a collection of quantitative analytical methods that take a theory-based, confirmatory approach to examining statistical relationships between measured (observable) and underlying (latent) variables. Despite originating from different scientific traditions, the apparent similarities between these two approaches hold promise for their combination in mixed methods research. This article contributes to the field of mixed methods research by exploring their potential synergies, how each approach could contribute to the other, and proposing a framework for their combinations in mixed methods research, which has implications in terms of the implied and explicit ontological and epistemological positionings of these two approaches.
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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 | 0.107 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".