<i>Hollingsworth v. Perry</i>: What Should the Court Do?
Bibliographic record
Abstract
Justice Anthony Kennedy faces a simple choice with profound consequences: When the Supreme Court considers the issue of marriage equality for gays and lesbians, does he want to write the next Plessy v. Ferguson 1 or the next Brown v. Board of Education? 2 As Justice Kennedy approaches the issue, he likely knows it is just a matter of time before gays and lesbians are accorded marriage equality in this country.Since the year 2000, eleven countries have begun allowing same-sex couples to marry: The Netherlands, Belgium, Spain, Canada, South Africa, Norway, Sweden, Portugal, Iceland, Argentina, and Denmark. 3 Last year, three more state legislatures, in Maine, Maryland, and Washington, adopted legislation allowing gays and lesbians to marry. 4 Recent opinion polls show that half of Americans now favor allowing gay marriage; 5 a 2011 poll found that 70% of Americans between the ages 18 and 34 support gay marriage.6 In light of this, Justice Kennedy has to know that a Supreme Court opinion rejecting marriage equality will be considered in hindsight to be as misguided as the infamous Bowers v. Hardwick ruling, which held that states could criminalize private, adult, consensual homosexual activity.7 Justice Kennedy wrote the opinion in Lawrence v. Texas, 8 overruling Bowers.In fact, Lawrence v.Texas was one of only two Supreme Court decisions in history advancing rights for gays and lesbians-the other was Romer v.Evans in 1996-and Justice
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.036 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".