Bibliographic record
Abstract
Because federal law does not expressly prohibit employment discrimination on the basis of sexual orientation or gender identity, LGBTQ Americans were thrilled to learn that a preliminary draft of the United States–Mexico–Canada Agreement (USMCA) included a provision (the Provision) requiring each nation to enact LGBTQ‐inclusive nondiscrimination laws. That excitement promptly turned to despair, however, after the Trump administration insisted on the addition of a footnote (the Footnote) designed to exempt the United States from the Provision. To date, the Footnote has been derided by scholars and trade experts alike as a transparent attempt to evade the Provision's LGBTQ‐inclusive mandate. Yet, by focusing only on what the USMCA does not do, these analyses overlook what the agreement does do, even if unintended, to benefit LGBTQ Americans. This article provides the first comprehensive analysis of the USMCA's implications for federal antidiscrimination law and demonstrates that—regardless of how the Supreme Court rules in a trio of LGBTQ employment cases—the Footnote actually stands to help, not hinder, the cause of LGBTQ equality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".