Cultural transmission and material culture : breaking down boundaries
Bibliographic record
Abstract
How and why people develop, maintain, and change cultural boundaries through time are central issues in the social and behavioral sciences in generaland anthropological archaeology in particular. What factors influence people to imitate or deviate from the behaviors of other group members? How are social group boundaries produced, perpetuated, and altered by the cumulative outcomeof these decisions? Answering these questions is fundamental to understanding cultural persistence and change. The chapters included in this stimulating, multifaceted book address these questions. Working in several subdisciplines, contributors report on research in the areas of cultural boundaries, cultural transmission, and the socially organized nature of learning. Boundaries are found not only within and between the societies in these studies but also within and between the communities of scholars who study them. To break down these boundaries, this volume includes scholars who use multiple theoretical perspectives, including practice theory and evolutionary traditions, which are sometimes complementary and occasionally clashing. Geographic coverage ranges from the indigenous Americas to Africa, the Near East, and South Asia, and the time frame extends from the prehistoric or precontact to colonial periods and up to the ethnographic present. Contributors include leading scholars from the United States, Canada, the United Kingdom, and Europe. Together, they employ archaeological, ethnographic, ethnoarchaeological,experimental, and simulation data to link micro-scale processes of cultural transmission to macro-scale processes of social group boundary formation, continuity, and change.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".