Population Size Does Not Predict Artifact Complexity: Analysis of Data from Tasmania, Arctic Hunter-Gatherers, and Oceania Fishing Groups
Bibliographic record
Abstract
A mathematical model purporting to demonstrate that the interaction population size of a group of social learners is a primary determinant of the level of technological complexity achieved by the members of that group through imitation of the most skilled individual in the group has been proposed. Empirical validation of the model has been attempted with archaeological data from Tasmanian hunter-gatherers and ethnographic fishing data from Oceania, but these data do not support the model. Data from a wide variety of hunter-gatherer groups show, instead, that implement complexity varies with an interaction effect between risk and number of annual moves and not with the interaction population size. Data from the Polar (Inuit) Eskimo and the Angmaksalik Inuit on the east coast of Greenland show that complex implements were part of both group’s technological repertoire even though each had interaction population sizes limited to a few hundred individuals, in direct contradiction with predictions from the mathematical model. The problem with the model lies in an invalid assumption.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".