Complexity, Complexity Reduction, and ‘Methodological Borrowing’ in Educational Inquiry
Bibliographic record
Abstract
Complex systems are open, recursive, organic, nonlinear and emergent. Reconceptualizing curriculum, teaching and learning in complexivist terms foregrounds the unpredictable and generative qualities of educational processes, and invites educators to value that which is unexpected and/or beyond their control. Nevertheless, concepts associated with simple systems persist in contemporary discourses of educational inquiry, and continue to inform practices of complexity reduction through which researchers and other practitioners seek predictability and control. In this essay, I examine a number of theoretical, practical and historical dimensions of complexity reduction in education and their implications for inquiry and action. I focus in particular on the ways in which some education researchers have reduced the complexity of the objects of their inquiries through ‘methodological borrowings’ from other research endeavors, such as borrowing a version of ‘evidence-based’ research from medical science, and borrowing the ‘triangulation’ metaphor from surveying.
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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 | 0.055 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.113 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| 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".