REPLY TO “COULD SIGN-BASED SEMANTICS AND EMBODIED SEMANTICS BENEFIT ONE ANOTHER?”
Bibliographic record
Abstract
Sign-based semantics and embodied semantics are argued to be mutually beneficial to one another. However, while the body does shape our cognitive activities to a great extent, this does not entail that cognition can be reduced to sensorimotor simulation, i.e that the mind can be reduced to the body. Language itself bears testimony to this, as the mind is construed in ordinary discourse as having the incredible capacity of being free to travel beyond the limits of present time and current spatial location. Nagel has argued famously that mind is a fundamental datum of nature that the materialist version of evolutionary biology is unable to account for, as consciousness has an essentially subjective character to it, a ‘what it is like for the conscious organism itself’ aspect, that cannot be reduced to the matter of which the organism is constituted. Two modern scientific developments refute the contention that the human mind can be explained as a purely material machine: quantum theory in physics and Gödel’s Incompleteness Theorem in mathematics. Just because the mind works through the body does not entail that the mind can be reduced to the body.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.039 | 0.039 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".