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 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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".