Reorienting Toward LGBTQ+ Belonging in Science, Technology, Engineering, and Mathematics by Feeling and Thinking With a Queer and Nonbinary Person in Virtual Reality
Bibliographic record
Abstract
ABSTRACT As part of the special issue Centering Affect and Emotion Toward Justice and Dignity in Science Education, this paper analyzes participants' experiences playing an immersive virtual reality (VR) experience that explores gender and sexuality‐based marginalization in STEM fields. The VR experience, designed and developed by the author and collaborators, addresses cisheteronormative ideologies embedded in science education by reimagining traditional STEM objects in queer representational forms while engaging players in a branching narrative dialog about gender and sexuality‐based oppression. My findings include three in‐depth analyses of participants' moment‐to‐moment interactions while playing the VR application. I argue that extending queer phenomenological approaches in education through the complimentary combination of ideological stance‐taking and emotional configurations can highlight how people become reoriented toward solidarity with marginalized people in moment‐to‐moment interactions. I focus my analysis on how participants' emotions, elicited by the VR experience, became the pivot for their ideological reorientations in solidarity with the VR narrator. In particular, the VR stories and research excerpts I have shared give concrete examples of how LGBTQ+ people are harmed in STEM learning environments. The analysis reveals how participants made choices in the branching narrative that showed emotional configurations of care toward the marginalized VR narrator and demonstrated recognition of how sociopolitical/socioscientific systems fail LGBTQ+ people. Further, I argue how designing learning environments that reorient learners toward social justice and solidarity with marginalized people could be productively used to engage people in challenging dominant cisheteronormative framings embedded in STEM education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".