Learning that Reflects the Living: Aligning Anticipation and Edusemiotics
Bibliographic record
Abstract
Joining educational philosophy with theoretical biology has come to form an important part of the growing edusemiotic movement. Edusemiotics has followed the example of biosemiotics (the understanding that the emergence of life is coextensive with the emergence of semiosis) to describe the process of learning itself as being coextensive with semiosis (or, sign-action). Following this recent turn in scholarship this paper argues for a perspective of learning rooted in the dynamics of the living. By ‘living’, I am referring to the integrated dynamics of reaction and anticipation that is definitional of living organisms, as distinguished from (non-living) inanimate matter. This calls for a theoretical perspective that transcends the realist/idealist divides often inherent in educational theory; offering a possible middle way between the constructivist emphasis on mind dependent reality, and the positivist emphasis on mind independent reality. Such a theoretical approach must be able to account for interactions in states of becoming, and thus calls for a broader causality than reductionist methods or computationalist accounts allow. To approach this re-conceptualization, I attempt to explore the combined relevance of two theoretical perspectives --- anticipatory biology, and the edusemiotic understanding of learning-as-semiosis. To address how anticipatory systems research from biology can be applied to learning theory, I first explore Nadin and Rosen’s notion of (Gödelian) G-complexity, and how this contributes to an understanding of the living as complex. Secondly, I address Peirce's notion of semiosis as it is embedded in his categorical system and overarching cosmology. In conclusion, I consider the confluences and differences between the concept of semiosis and the triadic relations that Peirce saw as fundamental to the origins of life, and the anticipatory processes that these theoretical biologists use to define living organisms, and examine how and if these two conceptions (taken in union) can potentially enrich theoretical accounts of learning. In this final analysis, the combined relevance of these two perspectives is applied to understanding the process of improvisation as an anticipatory/semiotic dynamic, to demonstrate the possible pedagogical relevance of this theoretical alignment
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".