America’s War on Same-Sex Couples and their Families: And How the Courts Rescued Them
Bibliographic record
Abstract
Daniel R. Pinello is a distinguished scholar whose past work has incisively argued that litigation has been an important ingredient in the struggle for transformative change in the United States. In this most recent book, he uses sexual diversity as his lens to reenter the long-standing debate over whether gains secured through courts produce anything other than shallow or pyrrhic victories. This new volume relies heavily on interviews with same-sex couples who live in six of the states where expansive “defense of marriage” (DOMA) measures were approved by referenda between 2000 and 2012. These “super-DOMAs” were designed to prohibit all official recognition of same-sex relationships, going far beyond marriage. Although these measures were largely undone by the U.S. Supreme Court’s 2015 decision in Obergefell v. Hodges, Pinella sets out to show how damaging such legal assaults were to the people he talked to while they were in force. Pinella is at his best when he asks how super-DOMAs were interpreted, in courtrooms and by advocates on either side. Near the end of the volume, in fact, the book’s strongest chapter considers in detail the Obergefell decision and lower court rulings preceding it. Here he highlights the irony that famous dissents by Justice Antonin Scalia helped judges, including many appointed by Republican presidents, strike down barriers to lesbian and gay 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".