Codepoietic Generation of Meaningful Information in the Evolving Biosphere
Bibliographic record
Abstract
Meaningful information represents reality in its potential form, and its actualization increases the system's negentropy. Biological evolution leads to the expansion of meaningful information by generating new coding systems (codepoiesis). Through this expansion, any evolutionary change obtains functional value when it receives an interpretation through which it gives rise to a meaningful function. Complexification in the evolutionary process corresponds to the generation of new meaningful information and, thus, to the development of new structures with corresponding functions. Any biological function has a meaning within the context of a particular environment, and the evolutionary search for new meanings results in the establishment of the state of sustainable non-equilibrium acting as an attractor, in which the developing system achieves the condition of maximization of its power via synergistic effects. At higher levels of the organization, evolutionary innovations emerge as niche constructions, behavioral choices, and, finally, the phenomenon of cognition. The evolutionary growth of meanings appears as a part of the expanding information system formed by the organisms inhabiting it. It acquires major expansion with the emergence of consciousness that incorporates the image of the whole world into the dynamic process of knowledge acquisition and creates the conditions for the development of global civilization.
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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 | 0.001 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".