The queer Hero versus the Fool bias of the queer trait: An archetypometric analysis of the collective portrayal of queerness in fictional stories

Jul 16, 2026

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Visibility in media plays a central role in identity development and in shaping public understanding of gender and sexuality. Although queer representation has grown, harmful stereotypes and recurring narrative tropes continue. This study quantifies how fictional television, film, and literature portray queerness by operationalizing archetypal roles (e.g., Hero, Diva, Outcast) and analyzing character traits along a straight-to-queer continuum using archetypometrics and Fandom LGBTQIA+ datasets. Results identify a collective-writing bias toward Fool for the straight-queer trait evaluation despite positive archetype content for highest queer-scoring characters.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages29

Reading Time1 h 13 min

Introduction

Methodology

Datasets

Procedures and measures

Results

Groups and traits

Trait distance

Archetype dimensions and membership

Trait × Archetype dimension

Group archetypes and ousiograms

Discussion

Limitations and future work

Concluding remarks

Appendices

Archetype dimensions

arXiv :2607 .08859v 1 [ cs .CY] 9 Jul 2026 The queer Hero versus the Fool bias of the queer trait: An archetypometric analysis of the collective portrayal of queerness in fictional stories Ashley M. A. Fehr 1∗ , Calla Glavin Beauregard 1 , Julia Witte Zimmerman 1,2 , Danny Benett 1 , Timothy R. Tangherlini3 , Christopher M. Danforth 1,4 , Peter Sheridan Dodds 1,5,6,7† 1 Computational Story Lab, Vermont Advanced Computing Center, Vermont Complex Systems Institute, MassMutual Center of Excellence for Complex Systems and Data Science, University of Vermont, Burlington, VT 05405, US 2 Computational Ethics Lab, University of Vermont, Burlington, VT 05405, US 3 Department of Scandinavian, Folklore Program, School of Information, Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA 94720-1500, USA 4 Department of Mathematics & Statistics, University of Vermont, Burlington, VT 05405, US 5 Department of Computer Science, University of Vermont, Burlington, VT 05405, US 6 Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, US 7 Complexity Science Hub, Metternichgasse 8, 1030 Vienna, Austria July 13, 2026 ∗ [ashley.fehr@uvm.edu](ashley.fehr@uvm.edu)[ ](ashley.fehr@uvm.edu)†[peter.dodds@uvm.edu](peter.dodds@uvm.edu) Abstract Visibility in media is pivotal for identity development and for broadening societal views of gender and sexuality. Queer representation has increased in recent years, yet damaging stereotypes and tropes persist. Here, we focus on queer portrayal and its perception by audiences in fictional stories (television, film, and literature) by studying characters by their quantified archetypes which are operationalizations of common conceptions such as Hero, Diva, and Outcast. We use the archetypometrics and Fandom’s LGBTQIA+ datasets to study samples of fictional characters along the trait differential spanning straight to queer. We find, quantify, and explain a seeming paradox. The characters with the highest queer score present positive primary archetypes and are typically Heroes rather than Fools, Angels rather than Demons, and Adventurers rather than Traditionalists. But evaluation across many stories for the straight-queer trait itself reveals a strong collective-writing bias towards Fool (away from Hero) and no meaningful loading for the other two dimensions. Our analysis offers a population-scale view of the complexities of queer portrayal, while also pointing to risks in blindly training on many-authored story corpora. Keywords archetypes, archetypometrics, traits, characters, straight, queer, representation, perception, stereotypes, stories, fiction, television Contents 1 Introduction 4 2 Methodology 5 2.1 Datasets .................... 5 2.2 Procedures and measures .......... 5 3 Results 7 3.1 Groups and traits ............... 7 3.2 Trait distance ................. 8 3.3 Archetype dimensions and membership ... 8 3.3.1 Trait × Archetype dimension .... 9 3.3.2 Group archetypes and ousiograms . 10 4 Discussion 10 4.1 Limitations and future work ......... 12 5 Concluding remarks 13 A Appendices A1 A1 Archetype dimensions A1 A2 Character subsets A7 A2.1 Subset methodology ............. A7 A2 .2 Character subset lists . . . . . . . . . . . . A9 A3 Traits and archetype membership counts A11 A3 . 1 Trait counts . . . . . . . . . . . . . . . . . . A11 A3.2 Archetype membership counts ........ A11 A4 Trait closeness A14 A5 Supporting figures A15 4 1 Introduction Visibility and media representation influence societal views of gender and sexuality [1, 2] . Favorable representations can contribute to a reduction in homophobic and sexist attitudes [3] . At the individual level, positive representations matter in identity development, especially for children and adolescents [4] . Supportive representations take on many forms such as attending to character depth or countering stereotypical representation

FAQs

What is the main paradox the study finds about queer portrayal?

Highest-queer-score characters tend to align with positive primary archetypes (e.g., Heroes rather than Fools), yet evaluation across many stories for the straight–queer trait shows a collective bias toward Fool and no meaningful loading on two other dimensions.

How do the authors quantify characters and their portrayal in fictional stories?

Characters are studied via quantified archetypes, using archetypometrics to operationalize common conceptions such as Hero, Diva, and Outcast, and then measuring trait differentials along a straight-to-queer spectrum.

Which types of media and datasets are used in the analysis?

The analysis targets fictional stories spanning television, film, and literature, using archetypometrics together with Fandom’s LGBTQIA+ datasets to sample fictional characters.

Jul 16, 2026

Upload by Miles