Bibliographic record
Abstract
Ethnographic hunter-gatherers exhibit fission-fusion cycles explained, for instance, as modular organisation of group sizes. However well models explain ethnographic pattern, archaeological tests pose challenges when we approach remote hunter-gatherers using what the !Kung teach us. We believe that eastern North American Paleoindians practiced fission-fusion, based partly on sites considered aggregations because they are unusually large and possibly organised as collections of smaller modules. Owing precisely to the flexibility that encompasses fission-fusion, however, large sites can be one-time aggregations or accumulations from repeated occupations. Seeking ethnographic pattern in material data presumes valid archaeological measures of contemporaneous group size and occupation span. Assemblage size and composition reflect group size and behaviour, but also span (itself parsed as aggregate or per capita) in ways not always appreciated. Surovell’s models of hunter-gatherer assemblage accumulation and methods to estimate span distinguish synchronic aggregation from diachronic accumulation in eastern North American Paleoindian data. This exploratory study applies Surovell’s models to Ontario’s Fisher Paleoindian site, where it both confounds and corroborates expectations based on simple correspondence between ethnographic and archaeological contexts.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".