New Evidence From Census 2020 on the Residential Segregation of Same-Sex Households: A Research Note
Bibliographic record
Abstract
The 2020 decennial census provides new insights into the demography of same-sex households and can shed light on ongoing debates in urban and gayborhood studies. Although the U.S. Census gives a vast undercount of the LGBTQ population, it is still the largest source of nationally representative data on same-sex households and is accessible over three time points (2000, 2010, 2020). In this research note, we use 2020 census data to examine the residential patterns of same-sex households down to the neighborhood level. By employing the index of dissimilarity, we present results for the 100 largest U.S. cities and 100 largest metropolitan areas that demonstrate moderate yet persistent segregation. In a continuation of prior trends, male same-sex households remain more segregated from different-sex households than do female same-sex households. We find moderate levels of within-group segregation by gender and marital status-representing new demographic trends. Finally, metropolitan areas have a higher dissimilarity index than cities, revealing greater levels of segregation when factoring in suburban areas. We discuss these trends in light of debates regarding the spatial organization of sexuality in residential contexts and outline future avenues for research utilizing recently released 2020 census data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".