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Record W4411404246 · doi:10.1071/ep24501

Session 23. Oral Presentation for: Complexities of First Nations impact assessments in offshore environments

2025· article· en· W4411404246 on OpenAlexaboutno aff

Bibliographic record

VenueAustralian Energy Producers journal. · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)Session (web analytics)Political sciencePublic relationsEnvironmental ethicsSociologyEnvironmental resource managementBusinessMedicineAdvertisingEconomics

Abstract

fetched live from OpenAlex

Presented on 28 May 2025: Session 23 Writing First Nations impact assessments (IAs) presents several complexities due to the unique cultural, historical and spiritual connections of First Nations peoples to the land and sea. Potential impacts from offshore developments on First Nations communities can be far-reaching and interconnected, making it challenging to accurately identify, measure and assess all potential consequences. These complexities underscore the importance of conducting thorough and culturally sensitive IAs to ensure the rights and interests of First Nations peoples are adequately protected. A fundamental step is to identify and understand how First Nations people’s cultural sites and values are intrinsically connected to the physical aspects of the environment that may be affected. This understanding can only be achieved through meaningful engagement with First Nations communities to identify and comprehend cultural sites and values. Long-term partnerships between titleholders and First Nations communities are essential to maintain the validity of information and management of the cultural sites and values. We will explore the complexities of defining First Nations people’s cultural sites and values and how offshore Australian titleholders have developed effective IAs and management strategies to protect the rights and interests of First Nations people. To access the Oral Presentation click the link below. To read the full paper click here

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 score0.019
Threshold uncertainty score0.823

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 communication0.0000.001
Open science0.0000.000
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 head0.036
GPT teacher head0.359
Teacher spread0.323 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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