Ecosystem accounting through first nations’ lenses: Integrating the SEEA-EA and Indigenous knowledge systems
Bibliographic record
Abstract
The UN System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA-EA) provides a framework for integrating information about the environment and the economy, organising information about ecosystems, measuring ecosystem services, and tracking change. We explore how SEEA-EA can incorporate First Nations' conceptualisation of nature and cultural connections to traditional lands. We identify multiple entry avenues, propose key principles and suggest steps to enhance relevance of the SEEA-EA to First Nations, principally: stock accounts should reflect aspects of Country that First Nations deem important; flow accounts should depict services they consider the most significant; and, stocks and flows should be measured using physical, subjective and monetary metrics that they deem appropriate. Respectful partnership with First Nations group(s) whose Country is being accounted for-centred on their priorities and values-would yield multiple benefits. We recommend that these ideas, alongside other possible approaches, be developed and tested with First Nations groups across diverse geographic and cultural contexts.
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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 | 0.001 | 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 | 0.016 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".