Civil Registration and Vital Statistics, Emergencies, and International Law: Understanding the Intersection
Bibliographic record
Abstract
Civil registration and vital statistics (CRVS) systems are typically run by governments to record every birth, adoption, death, marriage, and divorce that occurs among a country's population. Registration of vital events provides individuals with a formal relationship with the State and each other, and is the foundation of a person's identity, nationality, and legal status. At a population level, vital statistics are essential for effective planning and implementation of policies and services. Globally, strong CRVS systems are increasingly recognised as a crucial backbone for redressing health inequities and as a priority in strengthening global health and development efforts. Many countries, however, currently lack adequate and reliable CRVS systems, leaving many people vulnerable to statelessness, limited access to important government services (such as education and health services), and effective legal protection. Public health and humanitarian emergencies in such contexts can expose those already disadvantaged and marginalised to heightened risk. CRVS systems weakened by crises make registration difficult or impossible and unregistered people may be displaced or separated from their families, exacerbating their susceptibility. The presence of a strong CRVS system, therefore, can facilitate effective and cost-effective emergency responses, help prevent exploitation of individuals (particularly women and children), and help to rebuild communities post-crisis. This article will consequently review the international legal mandates that exist to strengthen CRVS systems globally, with particular view to public health and humanitarian emergencies. Identity and citizenship, and the socio-political contexts in which these concepts co-exist, are inevitably interconnected with CRVS. This can create potential for CRVS systems and data to be exploited as a political instrument. Grounding CRVS strengthening in a single binding, human rights law instrument is a potential way forward.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".