Bibliographic record
Abstract
ABSTRACT In this response to Dylan Paré's “Queer reorientations in virtual reality: Designing for solidarity in science and technology learning environments,” and as part of the special issue “Centering Affect and Emotion Toward Justice and Dignity in Science Education,” I invite educators to consider “the virtual” that always exists alongside actual reality. Drawing from recent research by Dylan Paré entitled “Reorienting Toward LGBTQ+ Belonging in Science, Technology, Engineering, and Mathematics by Feeling and Thinking With a Queer and Nonbinary Person in Virtual Reality,” I argue that things like solidarity, ethics, and justice are not possible without the existence of ‘the virtual’; which is part of the everyday existence of things, making the technology we call “virtual reality” but a tiny example of “the virtual.” Virtuality determines the lines of possibility that a being might take toward ethical becomings and different forms of actualization in the world. Using sociomaterialist philosophy this article encourages educators to explore the virtual for just futures and multispecies flourishing. Using the technology of virtual reality in the way Paré does is one way to open the wide potential of the virtual dimension. While virtual reality research for justice and inclusion might seem like a niche area of computer science, the learning sciences, or technology education, such research helps reintroduce educators and students to the vast aspects of reality that have not yet actualized but are nonetheless real and ever‐present.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.047 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.031 | 0.073 |
| 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".