Bibliographic record
Abstract
Numerous studies in the past 2 decades have recorded an apparently high rate of Staphylococcus aureus infection in so-called indigenous populations—in particular, a high rate of infection caused by methicillin-resistant strains of S. aureus (MRSA) [1]. Such problems have been documented among Native Americans [2], Pacific Islanders in Hawaii [3], Alaskan Natives [4, 5], aboriginal Canadians (First Nations, Métis, and Inuit) [6, 7], Western Samoans and other Pacific Islanders living in Auckland, New Zealand [8, 9], and Australian Aboriginals [10] In this context, the concept of indigenous populations stems from the modern histories of the United States, Canada, and Australia. These histories have many parallels, one of which is the displacement and resettlement of indigenous populations that had occupied these countries for the previous tens of thousands of years. These populations became the “Fourth World,” that is, the “Third World inside the First,” because their well being and health status have suffered as a result of their resettlement, often to remote communities and reservations. Notably, the infectious diseases burden in these communities has remained high or even increased, compared with that of the nonindigenous population, and it has contributed to a significantly shorter life expectancy. As a result, staphylococcal infections, both minor and serious, are a prominent part of the infectious disease burden in indigenous populations
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".