Bibliographic record
Abstract
While uncertainty remains as to the relative importance of the factors that propelled Neolithization at different sites, a model is gaining traction that proposes that cereal cultivation was adopted in part to produce alcohol for competitive feasting. The model ties together the emergence of two key phenomena – cereal cultivation and social inequality – and is supported by a variety of archaeological and ethnographic data. However pharmacological theory has not yet been explicitly deployed in the presentation of the model; rather its development has relied on a common-sense understanding of the effects of alcohol and its cross-cultural importance in social life. Our aim in this paper is to bring understandings of drug use from pharmacology and related disciplines to bear upon the challenge of explaining Neolithization. We find that pharmacological theory sheds light on the importance of alcohol and other mood-altering products of Neolithic farming. In particular there is support for some influence of pharmacoactivity on Neolithic social evolution, which might extend beyond a role in feasting to include modulation of responses to status hierarchies, increased residential densities, and more intense work schedules. We propose that pharmacological influences be incorporated into models of the Neolithic transition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".