Complexity Thinking and Methodology: The Potential of 'Complex Case Study' for Educational Research
Bibliographic record
Abstract
Complexity theories have in common perspectives that challenge linear methodologies and views of causality. In educational research, relatively little has been written explicitly exploring their implications for educational research methodology in general and case study in particular. In this paper, I offer a rationale for case study as a research approach that embodies complexity, and I explore the implications of a ‘complexity thinking’ stance for the conduct of case study research that distinguishes it from other approaches. A complexity theoretical framework rooted in the key concepts of emergence and complexity reduction, blended using a both/and logic, is used to develop the argument that case study enables the researcher to balance the open-ended, non-linear sensitivities of complexity thinking with the reduction in complexity, inherent in making methodological choices. The potential of this approach is illustrated using examples drawn from a complexity theoretical research study into curriculum change.
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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 | 0.158 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".