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Record W7081997266 · doi:10.36487/acg_repo/2515_33

Argyle diamond mine: a First Nations perspective on closure

2025· article· en· W7081997266 on OpenAlexaboutno aff

Bibliographic record

VenueMine closure · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsClosure (psychology)NegotiationGovernment (linguistics)Work (physics)Perspective (graphical)Mining industry

Abstract

fetched live from OpenAlex

The Gelganyem Group represents the Traditional Owners of Argyle diamond mine, in the remote East Kimberley region of North Western Australia. In November 2020, mining ceased at Argyle after 37 years of operations and producing more than 865 million carats of rough diamonds. Just as Argyle Traditional Owners had to pioneer new ways of working with mining companies when asserting their rights during exploration, and negotiating land agreements during mining, they are now leading the charge for the negotiation of positive outcomes for First Nations people in mine closure. There are many lessons to be learnt from Argyle’s closure: the first mine closure of its size globally. Argyle Traditional Owners are keen to ensure that mining companies and other First Nations groups: learn from the Argyle experience start mining negotiations with closure in mind understand the critical importance of an agreed closure vision understand what effective engagement looks like work to understand the cultural differences in approach to closure and to the value of land work closely with industry and government to ensure mine closure requirements are fit-for-purpose maximise First Nations engagement and participation in mine site restoration use closure to tell the story of Aboriginal leadership, connection to Country, and resilience. First Nations participation in mine closure is critical to ensure its success. The lessons learnt from the closure of Argyle diamond mine will help mining companies and First Nations people work together to improve mine closure outcomes for everyone.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.240
Teacher spread0.232 · 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 designNot applicable
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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