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Record W4379381812 · doi:10.1215/00141801-10266912

Beaver, Bison, Horse: The Traditional Knowledge and Ecology of the Northern Great Plains

2023· article· en· W4379381812 on OpenAlexaboutno aff
Dan Flores

Bibliographic record

VenueEthnohistory · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicArchaeology and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsBeaverAmerican westEcologyReading (process)Environmental ethicsHistoryEthnologyArchaeologyGeographyLawPolitical science

Abstract

fetched live from OpenAlex

In the early 1990s Grace Morgan’s PhD dissertation was a topic of considerable discussion at the University of Montana, where I had just arrived to work with students in environmental history, Native American history, and the American West. Morgan had answered a fundamental question in the history of the northern West. In the heyday of the fur trade, had the Native peoples of that region actually destroyed beaver—as so many peoples across much of America had done—to exchange for the goods of the Industrial Revolution? The answer from her research in Saskatchewan was a fairly definitive no, and the explanation, as my students and I discussed it, rested on the role beavers played in the ecology of the northern plains. Native people had long understood that in an arid landscape beavers often created and preserved the only dependable water available for travelers. No matter how much pressure European traders applied, groups like the Cree and Blackfoot bands knew better than to undermine a critical ecology that beavers alone maintained.Morgan passed away in 2016. Through the press of her career or some other inattention she never took her dissertation into print, an oversight this 2020 volume finally rectifies. One of the liabilities of Beaver, Bison, Horse, then, is that it rests largely on fieldwork and literature from the 1980s and early 1990s. Yet, reading this volume, somehow that does not seem to date it or detract significantly from it now. Because Morgan was an original thinker and a probing researcher (her field work largely focused on Qu’Appelle River Valley in Saskatchewan), this monograph from thirty years ago nonetheless is well worth spending time with and absorbing.The gist of Morgan’s insights come down to the following. Unlike First Nations peoples in the woodlands, whose absorption into the market economy via killing beaver for the fur trade is so well documented, people on the arid plains refused because of spiritual and ecological reasons. That did not mean, however, that they managed to avoid incorporation into the market. Instead, prairie groups focused their trade on wolf pelts, which in some respects is as surprising as their refusal to kill beavers. Later, when horses made it possible, bison robes pushed wolves into a secondary role. Morgan’s castigation of the lure of firearms in the trade, or even the role that luxury goods played, was not new in the early 1990s and isn’t now. But she is more negative about the transformation horses wrought on Native life than I expected. In my view her most useful addition to knowledge was her archaeological work in reconstructing the pre-horse seasonal movements of people on the northern plains as they followed bison into river valleys in the winter then used fire to lure the animals to open country advantageous for drives and jumps in the summertime. She renders that pattern vividly.James Daschuk was one of Morgan’s students and wrote the foreword here, and Cristina Eisenberg, an ecologist who identifies herself as “mixed Indigenous,” authored the afterword. They do an admirable job translating Morgan’s work into contemporary efforts on the part of both ecologists and tribes to restore beavers, bison, and wolves to twenty-first-century America. This, they argue—and I couldn’t agree more—is one of our principal modern tasks. For five centuries Old World cultures ignored American distinctiveness and moved heaven and earth trying to remake North America in the image of Europe. The myopia involved in that was epic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.291
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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