Is cultural logic an appropriate concept? A semiotic perspective on the study of culture and logic
Bibliographic record
Abstract
It is argued that (a) the question of ‘cultural logic’ is a valid inquiry for disciplines seeking to comprehend and compare mental processes across cultures, and (b) semiotics, as the science of studying signs and signification, is an appropriate means of approaching the question of cultural logic. It is suggested that a shift needs to be made in studying reasoning across cultures from the traditional value-oriented methods of judgment to a meaningoriented assessment. Traditional methods of cross-cultural comparison are suggested to be flawed in their attempt to develop a psychological account of why different cultural societies can draw different conclusions from ‘similar’ data, because they typically do not take into account the culturally-specific processes of ‘meaning’ and semiosis. These processes, it is argued, cause input data to develop differentially from one semiotic context to another. In other words, before reaching the cognitive processing level data is already shaped by the semiotic context, thus what is processed cognitively by two individuals in two cultural/semiotic contexts is no longer ‘the same.’ A semiotically conceived notion of cultural logic is therefore a crucial factor in any cross-cultural study of cognitive and psychological systems.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.071 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".