When Worlds Collide: Hunter-Gatherer World-System Change in the 19th Century Canadian Arctic
Bibliographic record
Abstract
Interactions between societies are among the most powerful forces in human history. However, because they are difficult to reconstruct from archaeological data, they have often been overlooked and understudied by understudied by archaeologists. This is particularly true for hunter-gatherer societies, which are frequently seen as adapting to local conditions rather than developing in the context of large-scale networks. When Worlds Collide presents a new model for discerning interaction networks based on the archaeological record, and then applies the model to long-term change in an Arctic society. Max Friesen has adapted and expanded world-system theory in order to develop a model that explains how hunter-gatherer interaction networks, or world-systems, are structured--and why they change. He has utilized this model to better understand the development of Inuvialuit society in the western Canadian Arctic over a 500-year span, from the pre-contact period to the early twentieth century. As Friesen combines local archaeological data with more extensive ethnographic and archaeological evidence from the surrounding region, a picture emerges of a dynamic Inuvialuit world-system characterized by bounded territories, trade, warfare, and other forms of interaction. This world-system gradually intensified as the impacts of Euroamerican colonial activities increased. This intensification, Friesen suggests, was based on pre-existing Inuvialuit social and economic structures rather than on patterns imposed from outside. Ultimately, this intense interacting network collapsed near the end of the nineteenth century. When Worlds Collide offers a new way to comprehend small-scale world-systems from the point of view of indigenous people. Its approach will prove valuable for understanding hunter-gatherer societies around the globe.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".