WHF Position Statement for United Nations Fourth High-Level Meeting-2025
Bibliographic record
Abstract
As the world gauges progress towards the SDG targets set for 2030, it is becoming evident that most countries are not on track to achieving them.Collective resolve among all nations and pooled global resources are needed to accelerate progress to reach as close to those targets as possible.It is also clear that commitment to those targets must continue beyond 2030 since many low-and middle-income countries (LMIC) will most likely experience rising burdens of non-communicable diseases (NCD) for some decades beyond the SDG dateline and to ensure commitment from the global community.This is especially true for target 3.4, including cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases, and mental health, which are responsible for over 43 million deaths worldwide every year, with 18 million dying prematurely before the age of 70 years, and also cause the majority of morbidity and disability. 1 This is because ongoing demographic, nutritional, and environmental transitions in those countries will result in an accelerated incidence of NCD in the future.The inequities between and within countries are huge.The probability of premature deaths in Western Europe and Canada is as low as 15 percent, while it remains as high as 52 percent in Sub-Saharan Africa.Over half of the world's population lacks access to basic health services, 2 and many low-income countries lack the financial resources necessary to elevate their healthcare resources to an
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.059 | 0.033 |
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".