Cooperation and Cattle Herding in Eighteenth Century Acadia: Implications for Archaeological Studies of Agropastoralism
Bibliographic record
Abstract
Anthropological studies of cattle management have frequently used nomadic open-range African pastoralists as models even when examining more sedentary agro-pastoralists relying upon combinations of crops and livestock that prevent or inhibit mobility. The relatively limited number of datasets on more sedentary agro-pastoralists makes it difficult to assess the suitability of this analogy when modeling and understanding herd dynamics in sedentary or semi-sedentary societies like those in the European Neolithic or pre-industrial colonies in North America. Census data on seventeenth- and eighteenth-century French colonists in eastern Canada and the northeastern U.S. reveal that household herds average fewer than eight individuals. Herds this small would have been dangerously close to collapse if animals were slaughtered and would not have had sufficient numbers to grow quickly. Using effective population size, a measure from wildlife biology, to assess the demographic and genetic health of wildlife populations, we demonstrate that Acadian herders were able to overcome the challenges of their small herds by participating in village or inter-village herd networks. Furthermore, we demonstrate that differences in herd management existed across the Acadian colonies and correspond, in part, to local involvement in cod fishing. We suggest this case study may provide a useful model for understanding prehistoric sedentary agropastoralism and the role of cooperation in prehistoric animal management decisions.
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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 | 0.001 |
| 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) | 0.001 | 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".