What drives the evolution of hunter–gatherer subsistence technology? A reanalysis of the risk hypothesis with data from the Pacific Northwest
Bibliographic record
Abstract
Recent studies have suggested that the decisions that hunter-gatherers make about the diversity and complexity of their subsistence toolkits are strongly affected by risk of resource failure. However, the risk proxies and samples employed in these studies are potentially problematic. With this in mind, we retested the risk hypothesis with data from hunter-gatherer populations who lived in the northwest coast and plateau regions of the Pacific Northwest during the early contact period. We focused on these populations partly because the northwest coast and plateau differ in ways that can be expected to lead to differences in risk, and partly because of the availability of data for a wide range of risk-relevant variables. Our analyses suggest that the plateau was a more risky environment than the northwest coast. However, the predicted differences in the number and complexity of the populations' subsistence tools were not observed. The discrepancy between our results and those of previous tests of the risk hypothesis is not due to methodological differences. Rather, it seems to reflect an important but hitherto unappreciated feature of the relationship between risk and toolkit structure, namely that the impact of risk is dependent on the scale of the risk differences among populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".