Families with more than one trans person: Investments and divestments in cisnormativity
Bibliographic record
Abstract
Abstract Objective To examine how having more than one trans person in a family facilitates investments in or divestments from cisnormativity. Background While there is now a robust body of literature on trans people's experiences with cisgender family members and vice versa, largely missing has been a focus on families where more than one person in the family is trans. Method This paper focuses on a subsample of 10 families from a large international qualitative longitudinal study conducted across six countries, focused on trans young people and their families. The paper draws on interviews conducted in 2022 and 2023 with families in which more than one family member was trans. Transcribed interviews were analyzed thematically. Results The themes developed indicate that while for some families having multiple trans family members may mean that some cisgender family members invest further in cisnormativity, for other family members the existence of multiple trans family members may encourage divestments from cisnormativity, to the benefit of trans young people. Specifically, themes focus on multiple trans family members highlighting cisnormativity, and conversely, multiple trans family members indicating likelihood of support and offering a safe haven. Implications The paper concludes by emphasizing that while encouraging divestments from cisnormativity should not be the work of trans people, it is nonetheless important that research continues to investigate the experiences of families in which more than one person is trans.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".