Bibliographic record
Abstract
Specific types of exchange and interaction are characteristic of various levels of sociocultural complexity. ( Kent Flannery 1972: 129 ) To avoid the chaos that would result if they were obliged to redistribute all materials to all of the populace at feasts, the elites developed market institutions. ( Brian Hayden 1993: 405 ) We left Sirjan for Kirman yesterday on a truckload of dried limes … going to Tehran and had to change over to a truckload of stovewood … We searched the bazaar and found plenty of large still-fresh muskmelons, in form and size much like those that are sent from Kabul to India, but these are sweeter. They are a common item of the fruit trade in the capital, and every Tehrani will accordingly tell you that the country's best muskmelons come from Isfahan. Anyone who has ever been in Khorasan will have quite a different opinion. ( Walter N. Koelz 1983:18, 48 ) In this chapter we consider ethnoarchaeological studies of trade and exchange, processes repeatedly implicated by archaeologists in the development of complex societies and societal evolution. Of the limited number available we choose five for special attention. These cover a wide range of socioeconomic complexity. Exchange, trade, and distribution Let us use “exchange” as a general term for the transfer of goods and services between people, reserving “trade” for forms that involve at least part-time specialists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".