The Social Science of Same-Sex Marriage: LGBT People and Their Relationships in the Era of Marriage Equality
Bibliographic record
Abstract
Showcasing research from across the social sciences, this edited volume seeks to provide readers with an empirically grounded sense of how many lesbian, gay, bisexual, and transgender (LGBT) people marry in the US and Canada, what their marriages look like, and how LGBT people themselves are impacted by marriage and marriage equality. Prior to marriage equality, lawmakers and activists across the political spectrum debated whether same-sex couples should have the legal right to marry, and likewise, academic research to date has focused mostly on the politics of same-sex marriage. However, this edited volume focuses on LGBT people themselves and their intimate relationships in the era of marriage equality. Including both quantitative and qualitative social science research, it features 14 primary chapters that examine a diverse set of topics, including demographic patterns in same-sex marriage and cohabitation, marital aspirations and motivations among LGBT people, arrangements and dynamics within same-sex relationships, and the legal benefits and informal privileges associated with marriage. The edited volume will be of interest to scholars across a wide range of disciplines, including sociology, psychology, child and family studies, communications, social work, and economics, while also offering valuable information for laypeople generally interested in families and/or LGBT studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".