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Record W4292314041 · doi:10.1177/25148486221119158

Decolonizing conservation? Indigenous resurgence and buffalo restoration in the American West

2022· article· en· W4292314041 on OpenAlexaboutno aff
Lindsey Schneider

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousScholarshipDecolonizationColonialismPolitical sciencePower (physics)Environmental ethicsHarmEthnologySociologyGeographyLawEcologyPolitics

Abstract

fetched live from OpenAlex

There has been a recent surge of interest in “decolonizing” conservation and natural resource management fields. Most of this scholarship, however, speaks to colonialism on a global scale and does not address conservation within modern settler colonial states such as the United States and Canada. This project focuses on the reintroduction of buffalo (bison) in the American West as an example of how even conservation efforts that purport to include, value, and share Indigenous perspectives can ultimately uphold settler colonial relations of power. Using an Indigenous mixed-methodology approach, it interrogates the discursive construction of buffalo as “America's great conservation success story” and highlights the ways in which conservation has historically worked to support colonial projects of Indigenous erasure and dispossession. Some contemporary buffalo restoration projects seek to include Indigenous people as stakeholders and/or collaborators with unique cultural interests in buffalo, but these efforts do not always embody the material shift in power relations that Indigenous scholars have identified as a key component of decolonization. For Indigenous people, buffalo are more than a keystone species with cultural import; they are relatives whose well-being is deeply entwined with our own. For landscape-scale buffalo restoration projects to engage in decolonization, they must seek to not only repair the harm done to tribal nations through buffalo eradication but also work to support Indigenous resurgence by transforming structures of power.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations18
Published2022
Admission routes1
Has abstractyes

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