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Record W2416464333 · doi:10.5539/jsd.v9n3p208

Are Australian Aboriginal Communities Adapting to a Warmer Climate? A Study of Communities Living in Semi-Arid Australia

2016· article· en· W2416464333 on OpenAlexvenueno aff
Digby Race, Supriya Mathew, Matthew Campbell, Karl Hampton

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersAustralian Government
KeywordsLivelihoodNatural resource economicsDilemmaPovertyEnergy povertyRenewable energyAridExtreme weatherBusinessConsumption (sociology)Climate changeEconomicsEnvironmental resource managementEconomic growthGeographyEcologyAgriculture

Abstract

fetched live from OpenAlex

<p>Communities around the world adapt to warming climates in a number of ways. Adaptations can often be energy intensive or dependent on expensive infrastructure to cope with harsh weather, so the use of renewable energy and energy efficient housing is becoming an increasing feature in conversations about climate change adaptation. The cost of energy for households continues to increase, with this cost adding considerable financial pressure on low-income households in both developed and developing countries. The concept of ‘energy poverty’ is gaining utility around the world to highlight the prevalent dilemma faced by low-income households that they cannot afford the level of energy use to maintain their desired livelihood. In regions of the world with extended periods of extreme weather, households can allocate as much as 20 per cent of their budget on energy consumption to maintain comfortable housing. Research by the authors indicates that effective adaptation must not add to the financial burden on low-income households, if the liveability of Australia’s semi-arid region is to be sustained.</p>

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.279
Teacher spread0.246 · 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 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

Citations14
Published2016
Admission routes1
Has abstractyes

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