Bibliographic record
Abstract
Abstract Artificial Intelligence (AI) has become a powerful new form of inquiry unto human cognition that has obvious implications for semiotic theories, practices, and modeling of mind, yet, as far as can be determined, it has hardly attracted the attention of semioticians in any meaningful analytical way. AI aims to model and thus penetrate mentality in all its forms (perception, cognition, emotion, etc.) and even to build artificial minds that will surpass human intelligence in the near future. This paper takes a look at AI through the lens of semiotic analysis, in the context of current philosophies such as posthumanism and transhumanism, which are based on the assumption that technology will improve the human condition and chart a path to the future progress of the human species. Semiotics must respond to the AI challenge, focusing on how abductive responses to the world generate meaning in the human sense, not in software or algorithms. The AI approach is instructive, but semiotics is much more relevant to the understanding of human cognition, because it studies signs as paths into the brain, not artificial models of that organ. The semiotic agenda can enrich AI by providing the relevant insight into human semiosis that may defy any attempt to model them.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".