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Record W2945699362 · doi:10.18260/2-1-370.660-114407

BLACKOUT: Teaching Students about the Power Grid through Experiential Workshops and Video Gaming

2019· article· en· W2945699362 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

VenueChemical Engineering Education · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsBlackoutRenewable energyElectricityElectricity generationGridEnvironmental economicsSolar powerEngineeringComputer sciencePower (physics)Electrical engineeringElectric power systemEconomics

Abstract

fetched live from OpenAlex

BLACKOUT! is a turn-based video game that introduces undergraduate and high school students to the types of power generation available in most electricity markets. The workshop portion of BLACKOUT! introduces students to the advantages and disadvantages of power generation by coal, natural gas, nuclear, wind and solar. The students then take the role of electricity providers in an open market. Players build power plants with capital and operating costs, estimate renewable production per turn (each turn is one hour of “real” time), and bid to sell their capacity to fulfill the grid demand, which changes every turn. The player that sells the most power by the end of the game is the winner. Survey data collected from high school students show significant improvements in perceived knowledge about the power grid after playing. BLACKOUT! is publicly available for classroom use and is accessible via http://psecommunity.org/LAPSE:2018.0136.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.468

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)0.0000.000

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 head0.009
GPT teacher head0.311
Teacher spread0.302 · 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