Beyond transitioning: committing to, exploring, and reconsidering transmasculine identity
Bibliographic record
Abstract
The current study investigated the components of gender identity development using a neo-Eriksonian process model. We aimed to derive and validate the distinct combinations of commitment, in-depth exploration, and reconsideration of commitment that characterise different states of self-understanding (identity statuses) experienced by transgender individuals in relation to their gender. To do so, we used two-stage clustering to categorise 354 transmasculine respondents on the Utrecht-Management of Identity Commitments Scale, expecting to produce statuses consistent with the theoretical solution of Crocetti and colleagues (2008). Partially supporting the hypotheses, we found that a four-cluster solution comprising Achievement, Closure, Moratorium, and Diffusion optimally captured transgender identity development, consistent with findings in lesbian and gay identity development by Kranz and Pierrard (2018). The current statuses related differentially to identity functions and elements of positive transgender identity which further supports their validity. These findings suggest that the experience of gender identity development for transgender people can be partitioned into meaningful statuses characterised by unique features.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".