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Enregistrement W4379381812 · doi:10.1215/00141801-10266912

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

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

Notice bibliographique

RevueEthnohistory · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueArchaeology and Natural History
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBeaverAmerican westEcologyReading (process)Environmental ethicsHistoryEthnologyArchaeologyGeographyLawPolitical science

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,100
Score d'incertitude au seuil0,198

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,015
Communication savante0,0040,004
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,291
Écart entre enseignants0,250 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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