Bioentropy, Aesthetics and Meta-dualism: The Transdisciplinary Ecology of Gregory Bateson
Bibliographic record
Abstract
In this paper I am going to be dealing with Gregory Bateson, a theorist who is one of the founders of cybernetics, an acknowledged precursor of Biosemiotics, and in all respects highly transdisciplinary. Until his entry into cybernetics Bateson was an anthropologist and like anthropologists of his day, accepted a semantic approach to meaning through the classic work of Ogden and Richards and their thought-word-meaning triangle. Ogden and Richards developed their semantic triangle from Peirce, but effectively turned the Peircian semiotic triad into a pentad of addressors and addressees, to which Bateson added context and reflexivity through feedback loops. The emergence of cybernetics and information theory in the 1940s increased the salience of the notion of feedback yet, he argued, information theory had truncated the notion of meaning. Bateson’s discussion of the logical categories of learning and communication distinguished the difference between and ‘sign’ and ‘signal’. Cybernetic signaling was a form of zero‑learning; living systems were interpretative and engaged in several logical types of learning. Twenty years later he took up similar sorts of issues with regard to the new science of ecology which had framed systemic ‘entropy’ solely in thermodynamic terms and ignored communication and learning in living systems. His concept of Bioentropy is presented in section two of this paper as is its association with redundancy. Bioentropy, in turn, led to his offering an entirely new definition of information: “the difference that makes a difference.” The definition could apply to both human and non-human communication patterns, since some forms of animal communication could not undertake logical typing. Finally, he believed that his own systemic approach was insufficient for meta-dualism. He promoted the idea of an ecological aesthetics which needed to be sufficiently objective to deal with the many disruptions in its own recursive relations, yet subjective and self-reflexive in the manner of a creative epistemology. ‘Rigor’ and ‘imagination’ became Bateson’s meta-logical types and aesthetics his meta-dualism. He drew his inspiration from the aesthetics of R.G. Collingwood. By mediating scientific rigor with Collingwood’s ‘imaginary’ Bateson brought about his own conception of mediated ‘thirdness’—different from C.S. Peirce—but one which brought cultural ‘mind’ more closely into association with ‘the mind of nature’.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".