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Record W2998202651 · doi:10.1177/2514848619898098

Environmental colonialism, digital indigeneity, and the politicization of resilience

2020· article· en· W2998202651 on OpenAlexaboutno aff
Jason C. Young

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersDivision of Social and Economic Sciences
KeywordsVulnerability (computing)ColonialismResilience (materials science)PoliticsIndigenousClimate changePsychological resilienceAdaptation (eye)Political scienceBureaucracyEnvironmental ethicsSociologyEnvironmental resource managementPolitical economyEnvironmental planningGeographyEcologyLawComputer securityPsychologySocial psychologyEnvironmental science

Abstract

fetched live from OpenAlex

While there is wide scholarly agreement that anthropogenic climate change has serious global implications, more debate exists around whether discourses of adaptation and resilience are effective at inspiring the necessary politics for addressing those implications. Resilience-based policies have been criticized for being overly techno-bureaucratic in nature, while leaving intact the deeper colonial and neoliberal logics that produce ecological destruction in the first place. This paper examines the Internet as a tool that Indigenous peoples are using to intervene in discourses of resilience, to mitigate the colonial impact that resilience and adaptation policies have on their communities. It does this through an exploration of how Inuit in Canada are leveraging digital technologies to engage in discussions about hunting and climate change in the Arctic. The paper argues that Inuit are engaging in digital forms of politics to re-scale their vulnerability beyond the local, to highlight dimensions of Arctic resilience beyond the “traditional,” and to intervene in the colonial relationships that produce environmental vulnerability in the first place.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.755

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 science0.0000.000
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 head0.012
GPT teacher head0.285
Teacher spread0.273 · 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.

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

Citations30
Published2020
Admission routes1
Has abstractyes

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