Towards a Geography of Unmarried Cohabitation in the Americas
Bibliographic record
Abstract
BACKGROUND: In the context of increasing cohabitation and growing demand for understanding the driving forces behind the cohabitation boom, most analyses have been carried out at a national level, not accounting for regional heterogeneity within countries. OBJECTIVE: This paper presents the geography of unmarried cohabitation in the Americas. We offer a large-scale, cross-national perspective together with small-area estimates of cohabitation. We decided to produce this map because: (i) geography unveils spatial heterogeneity and challenges explanatory frameworks that may work at the international level but have low explanatory power in regard to intra-national variation. (ii) we argue that historical pockets of cohabitation can still be identified by examining the current geography of cohabitation. (iii) our map is a first step toward understanding whether the recent increase in cohabitation is an intensification of pre-existing traditions or whether it has different roots that also imply a new geography. METHODS: Census microdata from 39 countries and 19,000 local units have been pulled together to map the prevalence of cohabitation among women. RESULTS: The results show inter- and intra-national regional contrasts. The highest rates of cohabitation are found in areas of Central America, the Caribbean, Colombia and Peru. The lowest rates are mainly found in the United States and Mexico. In all countries the spatial autocorrelation statistics indicates substantial spatial heterogeneity. CONCLUSIONS: Our results raise the question as to which forces have shaped these patterns and remind us that such forces need to be taken into account to understand recent patterns, particularly increases, in cohabitation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".