Bibliographic record
Abstract
The background of this paper starts with the history and significance of the author's relationship to the Campaign, U=U; Undetectable equals Untransmissable as an Indigenous woman and well-known advocate living with HIV. The methods used in this paper explored an adaptation of a thriving indigenous Health Framework implemented in New Zealand for over 40years. We anticipate the methods used in this paper along with the U=U Campaign will make the U=U relevant to other Indigenous Peoples. The common threads of the cultures are our creation stories and our rendition of the Health Circle or the Four Pillars. We interviewed and surveyed key community members, family, people living with HIV, and social workers that work in those communities over a period of 6months; 36 people participated. We shared personal stories anecdotally of her experiences. The results were a health model comparison of U=U from a Māori worldview. Each aspect of the Four Pillars or cornerstones of the model is explained from a personal experience perspective, which is inclusive and reflects a process familiar to Indigenous Peoples and worldviews. We are using stories to relay that information from that particular worldview. In conclusion, after much deliberation, discussions with key people, and personal experiences, we can tie the concept of U=U to an intrinsic framework that other Indigenous Peoples and communities can easily interpret.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".