Emerging from the Deep: Complexity, Emergent Pedagogy and Deep Learning
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
As indicated in the title – emerging from the deep – this paper proposes that an ability to face and deal with complexity can emerge from deep learning that is facilitated by pedagogies designed to ensure this outcome, especially an emergent pedagogy that instills deep education. Educators would view the classroom as a complex adaptive system (CAS) capable of self-organizing and operating at the edge of chaos where order emerges, just not predictably. Self-directed students would experience a learning environment that is appreciative of nonequilibrium, unpredictability, shifting and emerging patterns and co-evolution. Teachers would be coaches, activators and facilitators. Students would take part in learning encounters that ensure intellectual networking and conceptual connections. The knowledge and insight that develop would be interwoven and interdependent (complex), which is appropriate because complexity is needed to address complex problems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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