Please Stop Diagnosing Darth Vader with Borderline Personality Disorder to Teach Undergraduates about Neurodivergence (And Talk About Bipolar Zelda Instead)
Bibliographic record
Abstract
This article will examine the surprisingly prolific advocacy for the use of characters from the Star Wars franchise (1977-2024) to teach neurodivergence. While Star Wars is a specific example, it is utilized here as a focus for the larger issue within academia of the uncritical use of popular culture media for diagnostic pedagogy (defined here as the use of teaching methods designed to help students understand the processes of diagnoses). Responsible diagnostic pedagogy must be reflective of the immense power medical professionals have over their patients’ lives, avoid dehumanizing dis/ability, and incorporate the voices of people with dis/abilities. While Star Wars is a popular media franchise that students may engage with, its use in diagnostic pedagogy ignores dis/abled lived experience, intersectionality, and often relies on a gross misunderstanding of the text. This article will therefore explore how pedagogy utilizing the Star Wars franchise acts as a problematic example of the medical model that also ignores basic media theory. The case of bipolar Zelda (a pop culture phenomenon arising after the release of The Great Gatsby [2013]) will be analyzed as a far more productive counterexample. Specifically, this essay will argue that ‘bipolar Zelda’ succeeds where Star Wars fails because it invites intersectional discussions centering around issues of power and oppression.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".