Navigating megatrends::The ICPD Programme of action for a sustainable future
Bibliographic record
Abstract
▶ The Future of Population Data ▶ ICPD and Climate Action ▶ A Safe Digital FutureThe think pieces explore ways to sustain, refresh and accelerate ICPD commitments in a world of radical transformation.Designed for policymakers, they reflect on progress and highlight likely future scenarios.They offer starting points for discussion on what's next for population, development and sexual and reproductive health and rights including ending gender-based violence and harmful practices.This short summary highlights key findings and recommended actions on how to future-proof the ICPD Programme of Action (PoA) in the face of rapidly emerging digital technologies that serve to both advance and hinder progress.While technology has been a feature in human innovation for decades, a digitalized world has rapidly transformed the way in which technologies are designed and deployed for the benefit of individuals and society.It is undisputed that digitalization has enabled rapid economic growth and development in the last 30 years.Underpinned by profit-driven business models, however, the design and deployment of digital technologies may amplify existing inequalities with unique risks for women and girls in all their diversity.In a world increasingly characterized by digitalization and the rapid proliferation of technological innovation, the urgency to protect and continue to advance progress against the PoA cannot be understated.Safeguarding measures, innovating alternate business models, and effective and cross-jurisdictional regulation to protect, promote and respect human rights, including the principles of the ICPD, throughout the design and deployment of technologies, must be actioned to future-proof the PoA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.024 |
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".