Argyle diamond mine: a model for First Nations engagement
Bibliographic record
Abstract
The Gelganyem Group represents the Traditional Owners (TOs) 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 the production of more than 865 million carats of rough diamonds. Just as Argyle’s TOs pioneered new ways to engage during exploration and mining, they have now designed a robust engagement strategy to lead the charge for the negotiation of positive technical, environmental, economic and cultural outcomes for all parties in mine closure. Whilst most mining companies appreciate the critical need to engage with First Nations groups to agree a closure vision and strategy, many do not know how best to do this. Gelganyem’s closure work plan (CWP) model: There are many lessons to be learned from the Argyle closure; the first mine closure of its size globally. First Nations participation in mine closure is critical to ensure its success. The CWP model will help mining companies and First Nations people work together to improve mine closure outcomes for everyone. Argyle TOs are keen to ensure that mining companies and other First Nations groups benefit from the Argyle experience and maximise positive, meaningful and effective First Nations engagement and participation in mine closure planning, design and execution.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".