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Record W4307887189 · doi:10.1145/3549498

Feeling Good and In Control: In-game Tools to Support Targets of Toxicity

2022· article· en· W4307887189 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2022
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of SaskatchewanUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFeelingHarmToxicityPsychologyCoping (psychology)Variety (cybernetics)Internet privacyComputer scienceSocial psychologyPsychotherapistMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Game developers, researchers, and players recognize the harm of toxic behaviour in online games-yet toxicity persists. Players' coping strategies are limited to tools that focus on punishing toxic players (e.g., muting, blocking, reporting), which are inadequate and often misused. To address the needs of players experiencing toxicity, we took inspiration from research in other online spaces that provide support tools for targets of harassment. We iteratively designed and evaluated in-game tools to support targets of toxicity. While we found that most players prefer tools that explicitly address toxicity and increase feelings of control, we also found that tools that solely provide social or emotional support also decrease stress, increase feelings of control, and increase positive affect. Our findings suggest that players may benefit from variety in toxicity support tools that both explicitly address toxicity in the moment and help players cope after it has occurred.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.070
GPT teacher head0.368
Teacher spread0.298 · 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