Bibliographic record
Abstract
Writing First Nations impact assessments (IAs) presents several complexities due to the unique cultural, historical and spiritual connections of First Nations peoples to the land and sea. Potential impacts from offshore developments on First Nations communities can be far-reaching and interconnected, making it challenging to accurately identify, measure and assess all potential consequences. These complexities underscore the importance of conducting thorough and culturally sensitive IAs to ensure the rights and interests of First Nations peoples are adequately protected. A fundamental step is to identify and understand how First Nations people’s cultural sites and values are intrinsically connected to the physical aspects of the environment that may be affected. This understanding can only be achieved through meaningful engagement with First Nations communities to identify and comprehend cultural sites and values. Long-term partnerships between titleholders and First Nations communities are essential to maintain the validity of information and management of the cultural sites and values. We will explore the complexities of defining First Nations people’s cultural sites and values and how offshore Australian titleholders have developed effective IAs and management strategies to protect the rights and interests of First Nations people.
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 | 0.062 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".