Sexual and Reproductive Health in the Changing World: How close are the SDGs
Bibliographic record
Abstract
In the last quarter of 2023, the world is just recovering from the mighty blow of the COVID-19 pandemic that affects almost all aspects of human life including sexual and reproduction. At the same time, people from different parts of the world have been facing enormous challenges and violence like the Russia-Ukraine war, the Palestine-Israel war, the civil war in Myanmar, and conflicts in African countries like Sudan, and Burkina Faso. There were immediate past conflicts in several regions like Afghanistan, Azerbaijan, Iraq, Libya, Lebanon, Mali, Niger, Syria, and Yemen. The world has recently experienced a devastating pandemic with a global impact, the aftereffects of which are still being felt. The COVID-19 pandemic has caused significant mortality, disrupted the economy, increased poverty and undernutrition, and exhausted resources. These effects have drastically affected global visions for health and development.[1]
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".