Bibliographic record
Abstract
Bhaskar [1] proposed and argued for critical realism in natural sciences. Other authors have extended Bhaskar’s [1] arguments to include the social sciences [2], and to attempt to translate his philosophical position into useful research methodologies that can compete with positivist, constructivist, and pragmatic research paradigms [3]. Critical realism describes both intransitive and transitive components of human knowledge. It proposes underlying structures and mechanisms in the Real Domain that exist independently of human observers and possess the potential to govern events occurring in the Actual Domain [1]. Events that can be experienced and investigated as phenomena by human observers form a subset that occupies the Empirical Domain where researchers normally operate [1]. For critical realists, this positivist stance is balanced by the constructivist element to knowledge. As research is also a social activity, it results in knowledge regarding external structures and mechanisms that is also undeniably socially constructed [1] [2]. Critical realism [1] [2] claims to provide a philosophical and research paradigm solution to conflicts in ontology, epistemology, and axiology when conducting mixed methods studies [3]. Further, some researchers have advocated adopting a critical realism approach for synthesising the contributions of quantitative and qualitative data collected in such investigations, even in hindsight [4]. This paper considers the adoption of a critical realism approach late in a research project. A mini thesis drew together nine research articles in the field of teacher training and education that were published in peer reviewed journals between 2013 and 2019. These formed part of a PhD by published works submission [5]. The application of a critical realism approach as a triangulation of mixed methods data and findings using stages described by Bygstad and Munkvold [6] was found to be useful in formulating a concluding model for a complex project. However, a major criticism of the approach is also considered: that the same conclusions would have been reached following other research paradigm methodologies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.508 | 0.450 |
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".