Asking “How” and “Why” and “Under What Conditions” Questions: Using Critical Realism to Study Learning and Teaching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it