Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: In this speculative essay, we explore some of the implications and possibilities of complexity thinking for formal education. METHODS: We begin by developing the working definition that complexity research is the study of learning systems. Drawing on hard (rigorously empirical) complexity research, we critique some of the untenable assumptions and constructs that are typically used to frame those social enterprises that are attentive to adaptive, learning forms--including education, social work and health care. Looking to soft (holistic and more action-oriented) complexity research, we review some of the insights and advice that have arisen among educational researchers. This part of the discussion is framed by a brief description of an ongoing study of teachers' disciplinary knowledge of mathematics--specifically how complexity theory compels and enables us to grapple with the unique qualities of our 'object' of study, its emergence, its relationship to student understanding, and how it is implicated in such grander systems as culture and global ecology. CONCLUSIONS: We conclude by arguing that complexity theory might be properly construed as a theory of education, in contrast to the many theories that have been imported into and imposed on discussions of education over the past few centuries.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".