Exercising Agency Over Gender Diverse Identity: Experiences with Legal Name Change in Saskatchewan
Bibliographic record
Abstract
In recent years there has been an increase in individuals who identify two-spirit, transgender, non-binary, and gender non-conforming (Allen et al., 2020; Jones, 2022). Chosen name use is associated with improved mental health, and increased access to employment and housing (Crosby et al., 2016; Hill et al., 2018; Russell et al., 2018; Pollitt et al., 2019; Restar et al., 2020). Legal name is a natural step for 2STNBGN people to support chosen name use. Amidst a rise in anti-trans sentiment in Saskatchewan there is a need for research on the legal name change process. Using self-discrepancy theory (Higgins, 1987) an open-ended approach was used to explore the ways in which 2STNBGN people decide about and experience legal name change. Semi-structured interviews were conducted via Zoom with 2STNBGN people who resided in Saskatchewan, Canada. Convenience sampling through advertisements on social media resulted in a final sample of 15 2STNBGN people living across Saskatchewan ranging in age from 18-37, who had considered legal name change. Reflexive thematic analysis was used to explore participants’ past or anticipated experiences with legal name change, decision making about legal name change, and perceptions around gender identity. \n\nParticipants associated multiple challenges with the legal name change process (e.g., the publication requirement, complexity, cost, etc.). Perceived benefits of legal name change included recognition of their chosen name, self-confidence, and mental wellbeing. Accounting for the challenges and benefits, participants arrived at various decisions about legal name change based on their assessments and the extent to which legal name change may help resolve discrepancy between gender identity and perceived gender. While most participants felt that legal name change was worthwhile, some expressed that they could reduce gender dysphoria associated with their name in other ways. The results of this study suggest that policy changes (e.g., removing the publication requirement, simplifying the process, and removing or reducing parental/spousal permission requirements) could improve access to legal name change for 2STNBGN people. When 2STNBGN individuals legally change their name, they are protected from misnaming and can control when, how, and to whom they reveal their identity. Additionally, legal name change supports 2STNBGN peoples’ agency to claim and perform gender diversity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".