Bibliographic record
Abstract
This paper focuses on the integrative models of culture and cultural phenomena developed at the intersection of the cognitive and social sciences. It is argued that the leading research program of “cultural neuroscience” rests on the erroneous presuppositions with regard to the nature of cultural phenomena. Two alternative theoretical strategies are subsequently proposed for consideration. The first builds on the traditional computational approach in the philosophy of mind and cognitive sciences. According to this strategy, culture consists of information units as mental and public representations that are disseminated and transformed in the process of communication. The second strategy builds on a family of competing cognitivist approaches, namely the “4E” approaches. It asserts that culture is best explained in terms of individuals interacting in the shared material environment. The paper argues that the first strategy faces a number of substantial problems. It is claimed that the notions of information and mental content employed within this approach are scientifically questionable. In addition, it is maintained that the second strategy, although less conceptually mature and elaborate, does not face the same kinds of problems as the first one. In the concluding paragraph, the advantages and disadvantages of both theoretical strategies are, once again, weighed up.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.039 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| 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".