Bibliographic record
Abstract
There is now a developed and extensive literature on the implications of the ‘complexity frame of reference’ (Castellani & Hafferty, 2009) for education in general and pedagogy in particular. This includes a wide range of interesting contributions which consider how complexity can inform, inter alia, research on educational systems (Cochran-Smith et al., 2014; Radford, 2008) and theories of learning (Mercer, 2011; Fromberg, 2010), as well as work dealing with specific pedagogical domains including physical education (Atencio et al., 2014, Tan et al. 2010), clinical education and in particular the learning of clinical teams (Noel et al., 2013; Bleakley, 2010; Gonnering, 2010), and learning in relation to systems engineering (Thompson et al., 2011, Foster et al., 2001). This material has contributed considerably to my thinking about the subject matter of this essay which is not the implications of complexity for pedagogy but rather how we might develop a pedagogy OF complexity and, more specifically, a pedagogy of what Morin (2008) has called ‘general’ (as opposed to ‘restricted’) complexity. In other words how should we teach the complexity frame of reference to students at all appropriate educational levels?
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".