On Reinventing Education in the Age of Complexity: A Vygotsky-inspired Generative Complexity Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reinventing education is the ultimate aim of this contribution. The approach taken is a radical new complexity-inspired bottom-up approach which shows complexity as the fount of creativity and innovation. Organizing complexity accordingly may be the foundation for a new complexified vision of education. It all starts with new thinking in complexity about how complexity is actually generated in the real world. Such thinking offers new kinds of complexity like generative and emergent complexity. The approach taken is very much inspired by the genius of Vygotsky, as a visitor from the future. His focus was not only process-oriented, but also very much possibility-oriented. His method was bottom-up, and opened new spaces of the possible, like the Zone of Proximal Development. Yet he was not able to deal with the problem of complexity in his days. He ‘simply’ lacked an adequate causal framework, which showed causation as a generative bottom-up process, to be linked with potential nonlinear effects over time. He could not explain what he saw as possible: the turning points and upheavals of learning and development. In this contribution the focus will be on the link between the new thinking in complexity and the causal, generative nature of complexity in the real world. This link may show the ontological creativity of the entire world in general, and of human learning and development in particular. It may show the power of generativity to unleash this creativity by a new way of theorizing on education. The complexity-inspired theory of development as generative change, as thriving on the generative power of interaction, is fundamental and foundational for this new theorizing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it