Bibliographic record
Abstract
In this contribution the focus is on sketching a programmatic view of thinking in complexity about learning and development. This kind of thinking goes beyond linear thinking. The new thinking in complexity about a dynamic complex reality may enable us to build a new science of learning and education, which does not take the nonlinear complex reality for granted but regards it as “real”: a science with a framework that does not exist yet. A new vision on learning is presented which takes the concept of interaction as a key concept, which may be linked with the notion of dynamic complexity. Thinking in complexity has its focus on “that which is interwoven”. Learning and development through interaction may thus be viewed as a way of co‐creating ourselves within a web of reciprocal relationships with the other. This co‐creation may be described as a complex of self‐generative, self‐sustaining processes of mutual “bootstrapping” with potentially nonlinear effects over time. Modelling learning this way, may show learning to be a potentially nonlinear phenomenon within a new reality as the domain of possibilities and potentialities of learning. The modelling of such learning as “bootstrapping,” and the concomitant effects on both partners in the interaction, shows these very possibilities and potentialities of learning in their humanly connected spaces of possibility. It demonstrates the very truth of Vygotsky’s adage that “it is through others that we develop into ourselves.” Based on his thoughts, we are able to develop a new view of the complex nonlinear reality of learning and education, with learners as potentially nonlinear human beings.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".