Consensual Unions in Latin America: Persistence of a Dual Nuptiality System
Bibliographic record
Abstract
Nuptiality patterns have undergone significant transformations in the past decades. The increase in cohabitation is one of the trends that has recently attracted growing attention in the demographic and sociological literature. However, research has focused almost entirely on the developed world. This study highlights that cohabitation is not a phenomenon exclusive to Western industrialized countries; in fact, its prevalence is higher in Latin America and the Caribbean, where the proportion of unions built on a consensual basis ranges from 12 per cent in Chile to 62 per cent in the Dominican Republic. Consensual unions in this region have some distinctive features: their historical roots, their pervasiveness among all age groups, and their status as a socially accepted context for childbearing. This study documents the current prevalence of consensual unions in Latin America, examines recent trends, and explores how these “unregistered” partnerships fit into the family formation process, i.e. their stability and their role in relation to childbearing. Using data from the Demographic and Health Surveys (DHS) for 9 countries, we examine how women in legal and consensual unions differ regarding their socioeconomic background and their family formation trajectories. A logit analysis is performed to gain further insight into the factors associated with being in an informal versus a formal marriage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".