Critical Realism, Policy, and Educational Research
Bibliographic record
Abstract
This is a case for a critical realist approach to educational research and policy making. Such an approach enlists the full range of educational research tools to generate as broad an empirical picture of educational practices, patterns and institutional outcomes as possible. Its aim is to establish a comprehensive picture of an educational system at work, not just classical input/process/output descriptions, but also models of the life trajectories and capital of teachers and students to and through schools. The empirical work sets the table for theorising and modelling educational practice, for the interpretive and discursive work of policy formation. The translation of critical realist research into policy formation requires historical narratives and scenario planning, explanations about how things came to be, and about how alternative normative scenarios might be constructed. Here I want to provide a historical backdrop to the links between critical realism and a broader agenda of social justice and educational equity. Noting the parameters of current and recent research on pedagogy, achievement and social class, I emphasise the need for new sociological directions in pedagogy and in educational assessment and evaluation – but new directions built squarely on the foundations of social reproduction theory. In so doing, I want to suggest a way past the critical/empirical, qualitative/quantitative divide that has arisen in the context of Neoliberal educational politics in the US, UK and Canada. To address questions of generalisability, such an approach entails a shunting back and forth between levels of scale in a system. But moreover it requires a sociological imagination and critical hermeneutics for reading and interpreting evidence and 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.
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 | 0.042 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.113 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.016 |
| 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".