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Record W2969324812 · doi:10.1080/17450101.2019.1611015

Indigenous mobility traditions, colonialism, and the anthropocene

2019· article· en· W2969324812 on OpenAlexaff
Kyle Powys Whyte, Jared L. Talley, Julia D. Gibson

Bibliographic record

VenueMobilities · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsQueen's University
Fundersnot available
KeywordsAnthropoceneIndigenousColonialismSociologyEnvironmental ethicsAnthropologyGeographyEcologyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

The anthropocene is often discussed as an era of ‘new’ environmental changes that require unprecedented forms of societal adaptation, one example being climate-induced resettlement. Yet discussions of the anthropocene can also be better contextualized in terms of their featuring certain phenomena as ‘new’ that are really much more longstanding phenomena. For example, many Indigenous peoples have ancient traditions of environmental ‘mobility.’ This essay reviews some of the history of Indigenous philosophies, especially Anishinaabe, of mobility, migration, and resettlement. Often these philosophies focus on fluid and transformative relationships as constituting the fabric of resilient societies. Indigenous traditions of mobility are critically relevant for climate justice. They put into relief how colonial power can operate as a containment strategy that works to curtail mobility. In this way, looking at Indigenous mobility in the anthropocene involves unraveling layers of colonialism where containment has been widely imposed. This claim can be used to signal some of the dangers of centering the causal role of climate change in certain cases societal movement. To further support our claims, the essay concludes with a brief analysis of some of the literature and testimonies on resettlement in the Gulf of Mexico and Alaska.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.018
Scholarly communication0.0020.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.308
Teacher spread0.242 · 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 designTheoretical or conceptual
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

Citations111
Published2019
Admission routes1
Has abstractyes

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