The Use of Metapatterns for Research into Complex Systems of Teaching, Learning, and Schooling— Part I: Metapatterns in Nature and Culture
Bibliographic record
Abstract
We justify the concept of metapatterns as functional patterns or functional principles that are common to a large set of systems that encompass both biology and culture, by starting with the fact that evolved systems, whether biological or cultural, are produced from any iterative sequence of replication, variation, and selection. Therefore the systems that result, with specific functional parts, are formed as wholes that fit particular contexts. The principle of convergence in biological evolution, in which similar structures are independently evolved, is the model that can be extended even beyond biology. If the contexts of evolved systems across widely separated scales are similar, the resulting evolved systems can exhibit convergences that themselves occur at diverse scales. These grand convergences are the metapatterns. For example, the functional advantage of dynamically separating systems from their environments sets the context for the evolution of the metapattern of borders across various scales. We outline fifteen additional examples of metapatterns. We also examine the correspondences and differences between metapatterns as a multi-scale approach to systems and the approach from complexity science. We suggest that metapatterns could serve as tools for thinking about a diverse range of topics, and could thereby motivate the transference of generalizations. Finally, we propose that because metapatterns are employed in human thought, they will be useful in formulating new questions for education research, which is the subject of the companion paper.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".