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Record W4417474504 · doi:10.15353/cjds.v14i4.1292

Please Stop Diagnosing Darth Vader with Borderline Personality Disorder to Teach Undergraduates about Neurodivergence (And Talk About Bipolar Zelda Instead)

2025· article· W4417474504 on OpenAlexvenueno aff
Matthew Konerth

Bibliographic record

VenueCanadian Journal of Disability Studies · 2025
Typearticle
Language
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)DehumanizationFranchiseBorderline personality disorderStar (game theory)Popular culturePhenomenon

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.053
GPT teacher head0.385
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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