Bibliographic record
Abstract
Working with indigenous peoples provides unique opportunities for injury prevention Two articles in this issue of Injury Prevention concern injuries among indigenous peoples, the Navajo or Dine people of the southwestern United States, and the Aboriginal peoples of Western Australia.1,2 The issues raised by these studies have broad relevance. The United Nations estimates there are more than 300 million indigenous people living in over 70 countries. Among them are the estimated 50 000 Ainu people of Japan; 80 000 Saami people of Scandinavia, the Arctic, and Russia; 600 000 Aboriginal peoples of New Zealand and Australia; 3.5 million native peoples of North America (including tribes in the United States, the First Nations of Canada, and the Arctic’s Inuit peoples); 13.7 million nomadic peoples of Africa; and 148 million indigenous peoples of China and the countries of South and East Asia. Many of the challenges and solutions for working with indigenous people apply to research and action involving other culturally diverse populations, whether cross nationally or within national borders. There are a number of commonalities among indigenous peoples. These include cultures extending for thousands of years; experiences of exploitation, attempts at forced assimilation, and large scale neglect of human rights, health problems, and social needs; deeply held spiritual beliefs and practices; and increasing efforts to obtain international recognition and protection for their peoples and cultures. Equally important is the enormous diversity, even within individual countries. Canada is home to the Metis, Inuit, and over 600 First Nations peoples. There are more than 550 federally recognized tribes in the United States. When the first white settlers arrived in Australia, there were 300 000 Aboriginal peoples speaking over 250 languages. Also, there can be profound differences in lifestyle within individual tribes or cultural groups. To address the rising motor vehicle injury …
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".