Bibliographic record
Abstract
how to use the terms across a variety of issues confronting society.3 In the face of this discussion, American courts use sex and gender interchangeably.4 When courts apply the narrow definition of sex-that which is assigned at birth-courts affirm and perpetuate the gender binary, that of male and female.5 The gender binary excludes nonbinary individuals-those that do not identify as male or female.6 This exclusion is impermissible because it violates the Equal Protection Clause of the Fourteenth Amendment.7 Without a standard approach to the statutory interpretation of sex and gender, courts reach inconsistent decisions when faced with similar facts.8 This paper argues that 1 Mary Anne C. Case, Disaggregating Gender from Sex and Sexual Orientation: The Effeminate Man in the Law and Feminist Jurisprudence, 105 YALE L.J. 1, 4 (1995) ('"Sex refers to the anatomical and physiological distinctions between men and women; 'gender,' by contrast, is used to refer to the cultural overlay on those anatomical and physiological distinctions."). 2 See Jessica A. Clarke, They, Them, and Theirs, 132 HARV.L. REV.894 (2019) (discussing gender nonconformity); Andrea Meryl Kirshenbaum, "Because of . . .Sex": Rethinking the Protections Afforded Under Title VII in the Post-Oncale World 69 ALB.L. REV.139, n.8 (2006) ("Some courts define sex in the narrowest of terms as a biological distinction. ..Other courts have looked to a more holistic definition of sex to include gender identity.");Francisco Valdes, Queers, Sissies, Dykes, and Tomboys: Deconstructing the Conflation of "Sex," Gender," and "Sexual Orientation" in Euro-American Law and Society, 83 CALIF.L. REV. 3, 22 (1995).3 See Melinda D. Anderson, The Resurgence of Single-Sex Education, THE ATLANTIC, December 22, 2015 (discussing "the benefits and limitations of schools that segregate based on gender"); Dan Levin, North Carolina Reaches Settlement on 'Bathroom Bill', N.Y.TIMES, July 23, 2019; Vanessa Fuhrmans, What #MeToo Has to Do With the Workplace Gender Gap, THE WALL ST.J., Oct. 23, 2018 ("The #MeToo movement has thrown a glaring spotlight on the gender gap in the workplace.");Malika Andrews, How Should High School Define Sexes for Transgender Athletes?, N.Y.TIMES, Nov. 8, 2017 (explaining how "widespread disagreement over where the line should be drawn between sexes for purposes of athletic competition" is particularly challenging at the high school level); Keri Blakiner, Can We Build a Better Women's Prision?, The Washington Post Magazine, Oct. 28, 2019 (explaining that American prisons were built for men, and women have a different incarceration experience and if we want to see different outcomes, then we'd best incorporate different practices).4 Infra Section II.b; Schwenk v. Hartford, 204 F.3d 1187, 1201-02 (9th Cir.2000) ("[F]or purposes of [Title VII], the terms 'sex' and 'gender' have become interchangeable.")5 Infra Section IV.c.6 See Naomi Schoenbaum, The New Law of Gender Nonconformity, 105 MINN.L. REV.(forthcoming 2020) (quoting Kimberly A. Yuracko, Soul of A Woman: The Sex Stereotyping Prohibition at Work, 161 U. PA.L. REV.757, 795) (explaining that "partial gender nonconformers [] 'reject discreet aspects of their prescribed gender code while maintaining conformity with others"').7 U.S. CONST.amend.XIV § 1. 8 Infra Section II.b,Part III.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".