“I Don’t Like Passing as a Straight Woman”: Queer Negotiations of Identity and Social Group Membership
Bibliographic record
Abstract
For decades, sociological theory has documented how our lives are simultaneously produced through and against normative structures of sex, gender, and sexuality. These normative structures are often believed to operate along presumably "natural," biological, and essentialized binaries of male/female, man/woman, and heterosexual/ homosexual. However, as the lives and experiences of transgender people and their families become increasingly socially visible, these normative structuring binaries are called into stark question as they fail to adequately articulate and encompass these social actors' identities and social group memberships. Utilizing in-depth interviews with 50 women from the United States, Canada, and Australia, who detail 61 unique relationships with transgender men, this study considers how the experiences of these queer social actors hold the potential to rattle the very foundations upon which normative binaries rest, highlighting the increasingly blurry intersections, tensions, and overlaps between sex, gender, and sexual orientation in the 21st century. This work also considers the potential for these normative disruptions to engender opportunities for social collaboration, solidarity, and transformation.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.081 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".