Asking “How” and “Why” and “Under What Conditions” Questions: Using Critical Realism to Study Learning and Teaching
Bibliographic record
Abstract
Research paradigms offer a way for scholars to design, communicate, and reflect on their research effectively. A paradigm encapsulates the researcher’s worldview, including the epistemology, ontology, and axiology of the research. Researchers are often initiated, whether explicitly or implicitly, into particular paradigms through graduate study. This can cause difficulties in the multidisciplinary landscape of SoTL where practitioners either have to learn a new domain and/or communicate to peers outside their discipline. Learning about common research paradigms can help address these challenges. Four commonly used paradigms that have been proposed as relevant for SoTL research are post-positivist, critical realist, interpretive, and transformative (including indigenous). This article describes the basic tenets of critical realism and discusses them in relation to SoTL research. It i) describes key concepts within critical realism, including a stratified reality and a focus on causal mechanisms and the relationship between structure and agency, ii) explains how critical realism can be applied to studying learning and what this means for choice of SoTL methodology and method, and iii) describes the key aspects of two published SoTL studies. The paper concludes by suggesting that critical realism can enhance the theoretical rigor, practical utility, and interdisciplinarity of SoTL research.
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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 | 0.046 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.106 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".