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Record W4412045136 · doi:10.1145/3715668.3736374

Bold Sky: Co-Designing an Intimate Partner Violence Prevention Game

2025· article· en· W4412045136 on OpenAlexafffund
Veen Wong, Chris McNab, Laurie E. Jarvis, Cayley MacArthur, James R. Wallace

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer securityPsychologyHuman–computer interactionInternet privacy

Abstract

fetched live from OpenAlex

Intimate Partner Violence (IPV) intervention efforts have traditionally focused on reactive responses, often neglecting proactive strategies that address the harmful gender norms that can lead to IPV.This paper presents ongoing insights from Bold Sky.Bold Sky is an interactive narrative game co-designed with an interdisciplinary Advisory Committee comprising of young men, IPV experts, and game designers to foster gender-equitable attitudes and promote healthy relationships.Utilizing a participatory co-design approach, we engaged the Advisory Committee to iteratively develop the game.Preliminary findings from our first co-design session highlight three key design considerations for Bold Sky: 1) narrative complexity and avoiding didactic messaging enhance engagement; 2) perspective-shifting mechanics for gender transformative change; and 3) the role of game mechanics to support attitude changes.These insights inform the continued development of Bold Sky as we continue to develop and refine this digital IPV prevention tool.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.055
GPT teacher head0.423
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes2
Has abstractno

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