Bow and Arrow Technology in North America
Bibliographic record
Abstract
Abstract The adoption and spread of bow and arrow technology in North America reflect a complex interplay of ecological and social factors: While environmental variables such as wood availability and prey diversity/behavior were surely important, demographic and cultural variables—including population size, density, and connectivity; cultural transmission processes; and social dynamics—were equally or more influential. Parsing the relative effects of these factors and understanding interactions among them requires a clear view of the timing and nature of bow use across North America’s diverse geography. This paper makes two primary contributions to our understanding of the bow’s adoption in North America. Firstly, we present evidence for the bow’s earliest appearance, use in conjunction with other projectile technologies, and effects on economic and other systems in the North American Arctic, Pacific Northwest and Plateau, California and the Great Basin, Southwest, and Southeast. Secondly, we present a novel model of technological investment (uptake) that considers the effects of transmission agents’ social roles: Whether agents are craft specialists or do-it-yourself tool producers–users affects rates of adoption, a finding with global implications demonstrated here through regional case studies. We conclude that adoption depends not just on the bow’s inherent utility but on how tools are produced, shared, used, and valued in different economic systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".