Recognizing First Nations' values in natural capital accounting benefits all
Bibliographic record
Abstract
Abstract First Nations’ values are frequently overlooked in public and private sector decision-making. Natural Capital Accounting is increasingly promoted for decision-making but overlooks First Nations’ values, limiting its potential. Here, we present three Australian case studies highlighting the approaches, challenges, and progress made towards integrating First Nations’ values into accounting, aiming to distil lessons and help realize accounting’s potential to achieve transformative change in how decisions affecting First Nations people are made globally. We conclude that collaboration, respecting data sovereignty, and prioritizing First Nations’ voices are needed for comprehensive accounting. We recommend establishing an international working group under the auspices of the United Nations to include recognition of these values in accounting and how this recognition can inform decision-making. Recognizing First Nations’ values in Natural Capital Accounting benefits all by making these values visible and providing First Nations people, literally and figuratively, a “seat at the table” in the decisions affecting them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".