NESP RL Project 5.1 - Environmental economic accounting - developing and trialling approaches for accounting to ensure respectful inclusion of First Nations knowledges
Bibliographic record
Abstract
This project aims to address two overarching research questions: how can First Nations Peoples knowledges be reflected within (or alongside) Ecosystem Accounts prepared following the United Nations System of Environmental-Economic Accounting—Ecosystem Accounting (SEEA EA)? and what are suitable methods for appropriately gathering and presenting First Nations Peoples’ knowledges in relation to SEEA EA? To achieve the project aim, researchers will partner with two First Nations groups from contrasting contexts and environments (northern QLD savannah and southern WA coast). Together we will trial methods and prepare full sets of SEEA EA compliant accounts for each group. By co-designing methods and accounts presentation, the aim is for the accounts to provide information that is useful for the groups themselves in addition to providing useful information for other research users. Beyond accounts preparation, and incorporating existing Australian literature, the project will develop and test overarching learnings and recommendations that can inform future EA policy development within DCCEEW. Working closely with our First Nations partners, and testing outputs with a small number of First Nations people from other organisations, the project will seek to identify common themes across different First Nations groups, and assess the relevance of, and links between, national accounting information (such as NEAP) and accounts built upon First Nations knowledge and perspectives. The project is also linked with the Nature Repair Market team within DCCEEW and will exchange learnings and findings with those working in that space, thus seeking to promote complementarity between EA and Nature Repair Market developments.
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 | 0.212 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".