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Record W3017362285 · doi:10.1016/j.exis.2020.04.001

Fuelling regional development or exporting value? The role of the gas industry on the Limestone Coast, South Australia

2020· article· en· W3017362285 on OpenAlexaboutno aff
Thomas G. Measham, Lavinia Poruschi, Raymundo Marcos-Martínez

Bibliographic record

VenueThe Extractive Industries and Society · 2020
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersCommonwealth Scientific and Industrial Research Organisation
KeywordsContext (archaeology)SubsidyBusinessResource (disambiguation)Value (mathematics)Environmental resource managementNatural resource economicsIndustrial organizationEconomicsGeographyMarket economy

Abstract

fetched live from OpenAlex

The degree to which host regions benefit from resource extraction is a major issue for research and policy. In Australia and Canada, the dominant narrative of resource extraction is that most of the benefits flow away from host regions. This paper draws on evolutionary economic geography, presenting a case study of the Limestone Coast in South Australia, which previously extracted and distributed gas locally to food and fibre manufacturing industries. New policies seeking to renew the gas industry in the region, provide subsidies for exploration. Scenarios were developed to help inform decisions about the role of gas within this region. Qualitative analysis of the scenarios emphasised that gas needs to be affordable and locally accessible. Quantitative modelling showed that using the gas locally by manufacturing industries as part of broader industrial expansion would lead to greater benefits compared with exporting all gas outside the region. We conclude that policy settings have gone some way towards realising increased benefits for the region. Regional stakeholders clearly favoured the local use scenario but saw it as unlikely in the context of current infrastructure limitations. Stakeholders sought policy support for infrastructure to enable the preferred scenario to be realised.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.065
GPT teacher head0.240
Teacher spread0.175 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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