Digging beneath the Surface: When Disability Meets Gender Identity
Bibliographic record
Abstract
This article presents the results of a community-based participatory action-research conducted in the province of Quebec with 54 trans youth (15-25 years old). It describes the difficult reality faced by youth who are both trans and disabled and who live at the intersection of cisgenderism (or transphobia) and ableism. The research project, which uses the grounded theory methodology, was conducted in two phases of data collection between 2016 and 2019. In total, 39 of the 54 youth interviewed in person (72.2%) self-identified as disabled. This article therefore focuses on the experience of these young people. We begin this paper with a review of the literature on the theme of “transness and disability.” Then we present the core concepts in our research, including intersectionality, as well as the methodological framework that guided the project, grounded theory. In the following section, we present and discuss the research findings. After showing that, for trans youth, disability has implications at all levels in their lived experience and cannot be separated from their trans identity, we explore the intersections between transness and disability in the lives of trans youth through two main axes. We demonstrate how, on the one hand, impairments and ableism sometimes become obstacles to the realization of gender identity, and how, on the other hand, gender identity and cisgenderism can sometimes become disabling.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.035 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".