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Record W4387700644 · doi:10.1145/3617946.3617955

Fostering Collaboration and Advancing Research in Software Engineering and Game Development for Serious Contexts

2023· article· en· W4387700644 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.

Bibliographic record

VenueACM SIGSOFT Software Engineering Notes · 2023
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsHuawei Technologies (Canada)Kelowna General Hospital
Fundersnot available
KeywordsSustainabilityKnowledge managementComputer scienceGame testingGame DeveloperEngineering managementEngineeringGame designHuman–computer interactionGame design document

Abstract

fetched live from OpenAlex

The potential benefits of using the engaging and interactive nature of games to achieve specific objectives have been recognized by researchers and professionals from numerous domains. Serious games have been developed to impart knowledge, skills, and awareness in areas such as education, healthcare and the environment, while gamification has been applied to enhance the engagement, motivation, and participation of users in non-game activities such as sustainability and learning. As a result, the fields of game engineering, software engineering, and user experience are increasingly converging to create innovative solutions that blend the strengths of games with real-world applications.

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)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.369
Teacher spread0.310 · 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