Moving Through the Cis-tem: A Collection of Digital Stories Exploring Racialized Trans and Non-binary Experiences Navigating the Canadian Health Care System
Bibliographic record
Abstract
“Moving Through the Cis-tem” is the first qualitative study that was conducted by and for racialized transgender and non-binary people navigating the health care system. Grounded in the collective work of queer and trans community of colour, this project aims to creating more space for people to share their stories; to unearth what care looks like for those whose identities are at multiple marginalized intersections; and to investigate what it means to honour and respect peoples’ bodies. When we talk about gender-affirming care, we are actively resisting the medical discourses of what it means to be trans and non-binary. Three digital stories came out of this project and will be shared as a form of knowledge mobilization. This study is a direct continuation into the collective work of improving and advocating for racialized transgender and non-binary people to have access to all the types of care and support they need; to be heard and validated by health care providers; and to share their stories and lived experiences as they navigate through the health care system.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".