Bibliographic record
Abstract
This paper uses complexity theory as a means towards clarifying some of Gilles Deleuze’s conceptualisations in communication and the philosophy of language. His neologisms and post-structuralist tropes are often complicated and appear to be merely metaphorical. However their meanings may be clarified and enriched provided they are grounded in the science of complexity and self-organising dynamics. Reconceptualizing communication in a manner consistent with Deleuze’s philosophy enriches our understanding of the complexity involved in the process of learning and the whole of educational experience. The paper explores education as “becoming,” that is, a process of growth and becoming-other enabled by creative communication. While the mathematics of complexity is beyond the scope of this paper, some of its conjunctions with Deleuze’s philosophy will be examined for the purpose of addressing such problematic areas in education as, for example, specialisation and the breadth of curriculum. Finally, the paper moves to a practical level so as to construct an image of a (self-organised) classroom. Self-organising dynamics are posited as consistent with what Noddings (1993) called an excellent system of education. Education proceeds without any reference to an external aim. Rather, the “aim” is implicit in the experiential process of self-organisation and, as such, is conducive to students’ learning, creation of meanings, and eliciting broad curricula.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".