Bibliographic record
Abstract
Abstract Despite campaign promises to be the most “gay-friendly” Republican president, since assuming office, Donald Trump has been proactive in what many lesbian, gay, bisexual, and transgender (LGBT) advocates call a “rollback” of gains made during the Barack Obama administration, shocking many observers and bringing sexual and gender politics to the fore. How can we make sense of the contradictions and consequences of Trump's sexual and gender politics? I argue that examining the transnational processes of democratization, political homophobia, and homonationalism illuminates the significance of the administration's actions. A democratization approach reveals how Trump's reversal of Obama-era policies and appointment of conservative judges signifies a greater effort at de-democratization through the contraction of citizenship rights and weakening of the judiciary; political homophobia clarifies how the administration legitimizes its governance through opposition to LGBT people and issues with the appointment of openly homophobic and transphobic individuals to prominent positions; and homonationalism, or the entry of certain queer subjects into the nation at the expense of racialized “others,” aptly characterizes forms of queer inclusion still taking place under Trump. For these reasons, putting Trump's sexual and gender politics in transnational perspective can help us better understand this moment in U.S. politics.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".