Advancing legal identity, gender equity and women's empowerment via inclusive civil registration and vital statistics systems
Bibliographic record
Abstract
The 2030 Agenda recognizes universal legal identity, gender equity, and women's empowerment as essential for the realization of sustainable development, particularly in Goals 5, 16, and 17. This paper reviews the progress, key achievements, and ongoing challenges in developing legal identity systems that are universal and gender transformative. It highlights the strategic importance of inclusive civil registration and vital statistics (CRVS) systems as a tool in advancing gender equity and women's empowerment. Significant advances include increased birth registration coverage, the integration of marriage and divorce registration into legal ID systems, and efforts to reduce disparities in death registration by sex and socioeconomic status. The paper also explores the evolution of the CRVS data ecosystem, technical guidance, and CRVS data usage to advance sustainable development over the past 15 years, which have bolstered investment and technical cooperation in gender data for development. It highlights the critical role of civil registration and vital statistics systems in measuring and monitoring sustainable development indicators and promoting gender equity and women's empowerment. Despite progress, challenges remain in closing gender and social disparities in legal identity systems. The paper highlights promising cases of how CRVS systems have been harnessed to advance sustainable development and notes opportunities for further scaling CRVS systems strengthening efforts. It concludes by reflecting on the importance of counting everyone, because everyone counts, and the need for continued efforts to support and expand human capabilities for all.
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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 | 0.075 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".