Hunter-Gatherer Variability: Developing Models for the Northern Coasts
Bibliographic record
Abstract
Hunter-fisher-gatherer (HFG) variability has received a lot of attention. We review the key developments in the theories of variability, which have usually resulted in binary classifications. We argue that a range of variation based on the degree of territorial ownership is preferable to these classifications. Hunter-fisher-gatherers of the world’s northern coasts have only been partially explored in this way with regard to variability. A major reason for this is that such coastal groups use boats, so normative models of inland terrestrial foraging are not immediately applicable. We suggest that the Saxe-Goldstein hypothesis, the cautious linking of territoriality to funerary behaviour, may be a useful avenue to explore. Much work has been done by scholars of the northern coasts on boats and maritime transport, and some conclusions could be extrapolated to regions farther south.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".