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Record W2619340569 · doi:10.5250/9780803249813

Producing Predators: Wolves, Work, and Conquest in the Northern Rockies

2016· book· en· W2619340569 on OpenAlexaboutno aff
Michael D. Wise

Bibliographic record

VenueUniversity of Nebraska Press eBooks · 2016
Typebook
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCONQUESTPredationGeographyEcologyWork (physics)ArchaeologyBiologyHistoryEngineeringAncient history

Abstract

fetched live from OpenAlex

In Producing Predators, Michael D. Wise argues that contestations between Native and non-Native people over hunting, labor, and the livestock industry drove the development of predator eradication programs in Montana and Alberta from the 1880s onward. The history of these anti-predator programs was significant not only for their ecological effects, but also for their enduring cultural legacies of colonialism in the Northern Rockies. By targeting wolves and other wild carnivores for extermination, cattle ranchers disavowed the predatory labor of raising domestic animals for slaughter, representing it instead as productive work. Meanwhile, federal agencies sought to purge the Blackfoot, Salish-Kootenai, and other indigenous peoples of their so-called predatory behaviors through campaigns of assimilation and citizenship that forcefully privatized tribal land and criminalized hunting and its related ritual practices. Despite these colonial pressures, Native communities resisted and negotiated the terms of their dispossession by representing their own patterns of work, food, and livelihood as productive. By exploring predation and production as fluid cultural logics for valuing labor, rather than just a set of biological processes, Producing Predators offers a new perspective on the history of the American West and the modern history of colonialism more broadly.

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)0.000
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.268
Teacher spread0.231 · 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
GenreOther

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

Citations2
Published2016
Admission routes1
Has abstractyes

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