Bibliographic record
Abstract
Artificial intelligence output are undeniably creative, but it has been argued that creativity should be assessed in terms of, not external products, but internal self-transformation through immersion in a creative task. Self-transformation requires a self, which we define as a bounded, self-organizing, self-preserving agent that is distinct from, and interacts with, its environment. The paper explores how self-hood, as well as self-transformation as a result of creative tasks, could be achieved in a machine using autocatalytic networks. The autocatalytic framework is ideal for modelling systems that exhibit emergent network formation and growth. The approach readily scales up, and it can analyze and detect phase transitions in vastly complex networks that have proven intractable with other approaches. Autocatalytic networks have been applied to both (1) the origin of life and the onset of biological evolution, and (2) the origin of minds sufficiently complex and integrated to participate in cultural evolution. The first entails the emergence of self-hood at the level of the soma, or body, while the second entails the emergence of self-hood at the level of a mental models of the world, or worldview; we suggest that humans possess both. We discuss the feasibility of an AI with creative agency and self-hood at the second (cognitive) level, but not the first (somatic) level.
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 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 | 0.002 |
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; both teacher heads agree on what is shown here.
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".