“I would have preferred more options”: accounting for non‐binary youth in health research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a research team focused on vulnerable youth, we increasingly need to find ways to acknowledge non-binary genders in health research. Youth have become more vocal about expanding notions of gender beyond traditional categories of boy/man and girl/woman. Integrating non-binary identities into established research processes is a complex undertaking in a culture that often assumes gender is a binary variable. In this article, we present the challenges at every stage of the research process and questions we have asked ourselves to consider non-binary genders in our work. As researchers, how do we interrogate the assumptions that have made non-binary lives invisible? What challenges arise when attempting to transform research practices to incorporate non-binary genders? Why is it crucial that researchers consider these questions at each step of the research process? We draw on our own research experiences to highlight points of tensions and possibilities for change. Improving access to inclusive health-care for non-binary people, and non-binary youth in particular, is part of creating a more equitable healthcare system. We argue that increased and improved access to inclusive health-care can be supported by research that acknowledges and includes people of all genders.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it