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Record W2906303253 · doi:10.18061/dsq.v38i4.5991

Contests for Meaning: Ableist Rhetoric in Video Games Backlash Culture

2018· article· en· W2906303253 on OpenAlexaff
Hayley R. Crooks, Shoshana Magnet

Bibliographic record

VenueDisability Studies Quarterly · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRhetoricAbleismSociologyEconomic JusticeSocial psychologyPsychologyPolitical scienceLawGender studies

Abstract

fetched live from OpenAlex

An increasing number of video games focus on empathetic identification across difference. Since the mid-2000s, games that encourage catharsis and immersive engagement with trauma range from the personal as in That Dragon, Cancer (2014), in which players experience what it is like to parent a terminally ill child to geopolitical struggles as in Peacemaker (2007) which encourages player empathy for both sides of the Israeli-Palestinian Conflict. These games are rapidly gaining in popularity and commercial backing. As more games focus on issues of social justice, the backlash against these concerns among a vocal segment of the gaming community is increasing in frequency and intensity. A branch of the men's rights movement has focused on video games aimed at understanding difference, and has attracted attention suggesting that all those advocating for social justice in games (dubbed Social Justice Warriors) should be understood to have narcissistic personality disorder (NPD). We argue that these claims to NPD need to be understood as a form of structural ableism mobilized by the men's rights movement. In doing so, we argue that by situating the mental health labels evoked by current men's rights' activist rhetoric about feminist anti-racist interventions in game culture is a new form of the old practice of attaching mental health labels to people challenging social norms underpinning the dominant culture.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.365
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2018
Admission routes1
Has abstractyes

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