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Record W3027208343 · doi:10.14434/ijdl.v11i2.25020

Green Economy Game: A Modular Approach for Sustainable Development Education

2020· article· en· W3027208343 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

VenueInternational Journal of Designs for Learning · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsModular designGame designComputer scienceSustainable developmentVideo game developmentDesign thinkingProcess (computing)Game DeveloperSustainable designKnowledge managementHuman–computer interactionSustainabilityPolitical scienceEcology

Abstract

fetched live from OpenAlex

In this paper, we discuss our approach to designing a board game, the Green Economy, that promotes systems thinking. We anchored our game design process on design-by-analogy and rapid prototyping concepts by taking a modular approach to overcome the trade-off between realism and simplicity. The unique feature of the Green Economy enables players to change the rules of the game during the gameplay, which gives them a partial design opportunity. The theme, sustainable development, was chosen to challenge the players’ systems thinking in sustainable development. Systems thinking enables us to understand and face the complex challenges in global and networked social structures. Our design experience demonstrates the benefit of designing dynamic game elements that involve both strategic gameplay and game (re)design through systems thinking.

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)0.003
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.189
GPT teacher head0.400
Teacher spread0.211 · 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