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Record W2922223396 · doi:10.1080/13549839.2019.1590325

Old ways for new days: Australian Indigenous peoples and climate change

2019· article· en· W2922223396 on OpenAlexaff
Melissa Nursey‐Bray, Robert Palmer, Timothy F. Smith, Phil Rist

Bibliographic record

VenueLocal Environment · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsBrock University
FundersAustralian GovernmentNational Climate Change Adaptation Research Facility
KeywordsIndigenousClimate changeCorporate governanceColonialismAdaptation (eye)Political scienceAgency (philosophy)Traditional knowledgeTerminologyEnvironmental ethicsPolitical economySociologyGeographySocial scienceEcologyLawEconomics

Abstract

fetched live from OpenAlex

This paper explores how Australia's Indigenous peoples understand and respond to climate change impacts on their traditional land and seas. Our results show that: (i) Indigenous peoples are observing modifications to their country due to climate change, and are doing so in both ancient and colonial time scales; (ii) the ways that climate change terminology is discursively understood and used is fundamental to achieving deep engagement and effective adaptive governance; (iii) Indigenous peoples in Australia exhibit a high level of agency via diverse approaches to climate adaptation; and (iv) humour is perceived as an important cultural component of engagement about climate change and adaptation. However, wider governance regimes consistently attempt to “upscale” Indigenous initiatives into their own culturally governed frameworks - or ignore them totally as they “don't fit” within neoliberal policy regimes. We argue that an opportunity exists to acknowledge the ways in which Indigenous peoples are agents of their own change, and to support the strategic localism of Indigenous adaptation approaches through tailored and place-based adaptation for traditional country.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.171
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.011
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.003
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 head0.104
GPT teacher head0.282
Teacher spread0.179 · 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

Citations80
Published2019
Admission routes1
Has abstractyes

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