Press Q treats the Press Q dataset as interpretive material: useful, partial, situated, and always in need of care.
Principle
Press Q is a research prototype and does not claim exhaustive coverage of queer representation in games.
Principle
Quiu's responses are grounded in the available Press Q dataset, but mistakes may still occur and should be reviewed critically.
Principle
Representation categories and identity labels are socially, culturally, and historically contextual. Controlled vocabulary improves comparison but cannot replace a source’s specific language.
Principle
The project is designed to support discussion and inquiry, not produce definitive classifications of identity or value.
Use with care
Press Q should be read as a tool for guided exploration. The Press Q dataset helps organize evidence, but representation remains messy, lived, historical, and dependent on interpretation.
Known limitations
Press Q begins with cases that were discoverable through existing archives, scholarship, journalism, communities, playthroughs, and available online evidence. Obscure, regional, older, delisted, inaccessible, or poorly documented games are easier to miss.
Dialogue, romance options, localization, censorship, patches, downloadable content, remasters, and platform restrictions can change what is present. A record applies only as far as its documented version supports it.
Any schema simplifies. Broad categories can obscure differences within identities; specific categories can fragment related experiences. Press Q therefore shows examples, notes, missing values, and overlap rather than treating categories as natural or permanent.
Official statements, game text, wikis, criticism, forum discussion, and videos offer different forms of support. Evidence confidence records this difference but cannot remove all curatorial judgment.
Press Q does not know the complete population of every game ever released or every queer case within it. Corpus percentages therefore describe documented Press Q records, never the prevalence of LGBTQ+ content across the medium.
Identity and interpretive care
Specific supported terms are preferred over broad assumptions. Trans men are men and trans women are women; trans-specific coding preserves a relevant dimension rather than constructing a separate, opposing gender.
Press Q does not infer race, ethnicity, religion, disability, gender, sexuality, or creator identity from names, appearance, voice, mannerisms, or stereotypes. Unknown remains unknown.
Queer readings can be culturally important even when contested or creator-refuted. Preserving them with counterevidence avoids both erasure and false presentation as canonical fact.
Quiu and responsible AI use
Quiu is instructed to answer from the three Press Q datasets rather than general model memory. If a detail is absent, the appropriate answer is that it is not currently recorded.
Quiu must keep character identities, game-system possibilities, and queer readings separate. It must not convert a mechanic or interpretation into canonical character identity.
Quiu should reply in the language of the latest question and preserve research status, confidence, counterevidence, version, and other material limitations.
Grounding reduces unsupported claims but does not guarantee accuracy. Users should inspect source records before publishing, teaching, or making consequential claims from a generated answer.
Quiu can still misunderstand a question, format a field poorly, or produce an answer whose emphasis is misleading. Its output should be treated as an assisted summary of the available records, not an independent authority, diagnosis, or substitute for reading the cited evidence.
Fair use statement
Press Q is a nonprofit educational and research prototype. That purpose is relevant but does not automatically make every use fair. Under 17 U.S.C. Section 107, fair use is a case-specific balancing analysis that considers all four factors below. Credit and citation are good scholarly practice, but attribution alone does not create legal permission or guarantee fair use.
Purpose and character: the use should support criticism, comment, scholarship, teaching, or research and add analytical context rather than substitute for the original work.
Nature of the work: the factual or creative nature and publication status of the source material remain relevant to the analysis.
Amount and substantiality: use only what is reasonably necessary for the research purpose, including attention to whether the portion is qualitatively central.
Market effect: consider whether the use substitutes for, harms, or usurps an existing or reasonably likely market for the original work.
This project statement is a research practice, not legal advice. Image and quotation use should be reviewed individually; permission, public-domain material, open licenses, or removal may be more appropriate when the fair-use rationale is weak. Rights holders can request review of a specific asset and its source information.
Sources and framework
These sources inform the project’s definitions and safeguards. They do not make Press Q exhaustive, peer reviewed, or free from curatorial judgment.
Shaw, Adrienne, et al. “About (Please Read First!).” LGBTQ Video Game Archive.
Supports the distinction between explicitly coded content, creator statements, queer readings, and incomplete research coverage.
GLAAD. “Glossary of Terms.” Where We Are on TV 2023–2024.
Provides working definitions for terms including bisexual, nonbinary, queer, and transgender while emphasizing respectful, specific language.
D’Ignazio, Catherine, and Lauren F. Klein. Data Feminism. MIT Press, 2020.
Grounds the project’s attention to power, context, plural perspectives, and the limitations of binaries and apparently neutral classifications.
Crenshaw, Kimberlé. “Demarginalizing the Intersection of Race and Sex.” University of Chicago Legal Forum, 1989.
Provides the foundational account of why axes of power and identity cannot always be analyzed as isolated, mutually exclusive categories.
Tabassi, Elham. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1, 2023.
Informs the project’s emphasis on transparent, accountable, explainable, and human-reviewed use of AI-assisted outputs.
U.S. Copyright Office. “Fair Use Index.”
Explains the four-factor, case-specific framework under Section 107; nonprofit or educational purpose alone does not guarantee fair use.
