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Record W2747554055 · doi:10.1071/aj09061

Successful strategies for native title and Aboriginal cultural heritage approvals—an examination through the development of the Queensland CSG and LNG industry*

2010· article· en· W2747554055 on OpenAlexaff
Gavin Scott, Leonie Flynn

Bibliographic record

VenueThe APPEA Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsDawson College
Fundersnot available
KeywordsLegislationCommonwealthIndigenousNegotiationCultural heritageLegislaturePolitical scienceEngineeringPublic administrationEnvironmental planningLaw

Abstract

fetched live from OpenAlex

2009 saw an unprecedented level of engagement between oil and gas companies in Queensland and Aboriginal groups, primarily because of Queensland’s burgeoning CSG/LNG industry. Most proponents have had to deal with native title and Aboriginal cultural heritage arrangements with multiple parties simultaneously, often in the early stages of project developments where project certainty is low. Many native title parties have also had to deal with multiple projects at the same time. This has added an extra layer of complexity to what is already a difficult negotiation and regulatory landscape. Queensland and Commonwealth legislation impose a complex system of regulatory approvals governing the interaction of Aboriginal interests and oil and gas projects. Project proponents must comply with state petroleum legislation and Commonwealth native title legislation to ensure approvals are validly granted. This paper will examine the complex legislative and regulatory hurdles that have been faced by project proponents in the Queensland CSG/LNG industry in managing native title and Aboriginal cultural heritage issues. The paper will critically analyse the generally accepted strategies being implemented to address native title and Aboriginal cultural heritage issues. This will include a specific focus on the legal requirements to obtain indigenous land use agreements, the fundamental issues required to be addressed to achieve the authorisation and registration of these agreements, and the alternative options if it is not possible to obtain these agreements. Finally, the paper will conclude with some commentary on the legal aspects of managing Aboriginal cultural heritage.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.136

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 studies0.0000.000
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 head0.017
GPT teacher head0.266
Teacher spread0.250 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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