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Record W3096921969 · doi:10.1145/3383668.3419920

Illuminate

2020· article· en· W3096921969 on OpenAlexaffabout
Tina Chan, Adam Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAction (physics)StorytellingFeelingGriefGrounded theoryResource (disambiguation)Key (lock)Climate changePsychologySociologyComputer scienceSocial psychologyQualitative researchEcologyPsychotherapistSocial scienceNarrativeComputer security

Abstract

fetched live from OpenAlex

Increasing negative media about climate change and ecological loss has evoked feelings of hopelessness and loss for the future in youth. We created Illuminate in response, a mobile game which leverages resource management and storytelling to instill grounded hope and inspire climate action for youth in Canadian classrooms through climate simulations. Illuminate tackles the under-explored topic of eco-grief from media in HCI and asks players to informatively invest in environmental strategies which reflect real life economic costs and environmental impacts in key Canadian contexts. This paper describes Illuminate's conception to release and presents a game designed to motivate learning through meaningful simulations for grounded hope and action.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.035
GPT teacher head0.280
Teacher spread0.245 · 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 designNot applicable
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

Citations9
Published2020
Admission routes2
Has abstractyes

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