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Record W3013647951 · doi:10.69520/jipe.v2i1.73

Gaming as a Form of Experiential Learning: Career Ready Project—Bloom Virtual Village

2019· article· en· W3013647951 on OpenAlex
Deb Bonfield, Pamela Gauci, Dario Guescini, Jackie Tan, Apostolo Zeno

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

VenueJournal of innovation in polytechnic education. · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsExperiential learningBloomPsychologyMultimediaComputer scienceMathematics educationGeologyOceanography

Abstract

fetched live from OpenAlex

George Brown College students, in collaboration with industry partners Baycrest Health Sciences and Microsoft, worked with experts to imagine, design, create, validate and employ simulated field experience models. Under close supervision of George Brown’s faculty from two academic divisions, students converted the field placement experience in long-term care into a simulation gaming solution. Upon completion of this initial pilot, George Brown College will begin to integrate the game in several programs in our Centre for Community Services and Health Sciences. Students worked collaboratively to develop a unique digital experiential learning opportunity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.351
Teacher spread0.325 · 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