Bibliographic record
Abstract
The notion of culture implies the relative stability of sets of algorithms that become entrenched in human brains as children become socialized, and, to a lesser extent, when immigrants become assimilated into a new society. The semiotics of culture has used the notion of signs and systems of signs to conceptualize this process, which takes for granted memory as a natural affordance of the brain without raising the question of how and why cultural signs impact behaviour in a durable manner. Indeed, under the influence of structuralism, the semiotics of culture has mostly achieved synchronic descriptions. Dynamic models have been proposed to account for the action of signs (e.g., semiosis, dialogism, dialectic) and their resulting cultural changes and cultural diversity. However, these models have remained remarkably abstract, and somewhat disconnected from the actual brain processes, which must be assumed to be involved in the emergence, maintenance, and transformations of cultures. Semiotic terminology has contributed to a systematic representation of cultural objects and processes but the philosophical origin of its basic concepts has made it difficult to construct a productive interface with the cognitive neurosciences as they have developed and achieved notable advances in the understanding of memory over the last few decades. The purpose of this paper is to suggest that further advances in semiotics will require a shift from philosophical and linguistic notions toward biological and evolutionary models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".