Towards Inclusive Infrastructures: Examining the Experiences of Gender Diverse People in University Systems
Bibliographic record
Abstract
Research on gender, identity, and technology has focused on designing individual systems for inclusivity, that is, examining how systems can represent transgender, nonbinary, gender non-conforming, and other gender diverse identities in affirming ways. Less is known about how infrastructures -that is, multiple loosely connected and long-lived systems-can be made inclusive. This gap is critical to address as systems rarely operate in isolation and infrastructures are pervasive in contemporary organizations. For example, universities maintain separate systems for applications, admissions, housing, and course management that only occasionally communicate with each other. This paper steps towards addressing this gap by examining the challenges gender diverse people face in navigating such complex infrastructures. Through a qualitative study consisting of 20 interviews, we find that participants encountered challenges unique to interconnected systems-such as slow and inconsistent updates to personal information and unexpected leaks of outdated information-that together created a perpetual risk of being misgendered by both systems and other users. To navigate such challenges, participants used workarounds such as seeking help from organizational insiders, piloting identity changes on a small scale, engaging in small acts of resistance, and strategically choosing which battles to fight. Participants expressed several goals for inclusive infrastructures: quick and consistent personal data updates; transparency around non-inclusive design decisions; safe and contextual data sharing practices; and community education to facilitate broader cultural change. We discuss novel insights on identity, representation, and inclusion, and present design implications to better support gender diverse people in technical infrastructures.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".