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Record W2651948055 · doi:10.1093/medlaw/fwx021

Civil Registration and Vital Statistics, Emergencies, and International Law: Understanding the Intersection

2017· article· en· W2651948055 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Law Review · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsPopulationGeographyPolitical scienceEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.320
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it