Bibliographic record
Abstract
This paper explores the idea that affects make up a semiotic system. After briefly presenting a version of modern semiotic theory—that of C. S. Pierce—the paper describes affects as sign systems of biological evaluation. Each of the major affects has a kind of vertical structure from brainstem to cortex and represents an analog evaluator in terms of one of the vectors important to survival. Each affect is a semiotic system in which there is a physiological bodily response, a display via the face and body, and a subjective awareness. The entire package leads to dispositions to approach or avoid and to have other evaluative subjective feelings. In nonhuman species, affects are represented by nonsymbolic signs. Humans have, in addition to the nonsymbolic, the symbolic capacity to name and speak about affects. The paper explores the advantages and possible functions of this capacity with respect to being aware of affects and finding evolutionary advantage in this awareness. Looking at this subject from the semiotic point of view allows us to shed light on: (1) the issue of the time course of an affect; (2) the concepts of drives and affects; (3) the three manifestations of affect and their dissociation; and (4) the different functions of the three types of signs with respect to affect.
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 | 0.001 | 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 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".