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Record W2324197570 · doi:10.5539/ijms.v8n2p13

Objective: Winning or Learning? A Study of Marketing Simulation Games

2016· article· en· W2324197570 on OpenAlex
Myriam Ertz

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueInternational Journal of Marketing Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBusiness simulationPerspective (graphical)MarketingComputer scienceManagement scienceKnowledge managementEconomicsBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

<p>Simulation Games are now broadly used by scores of business schools, especially in marketing. These games favour active, feedback-based learning, normally in groups, and exhibit characteristics of intrinsic motivation channelled into a learning perspective. If Simulation Games clearly spur individuals to “win”, it is more difficult to assess whether they effectively empower them to “learn”. This study is a literature review that examines the limitations of learning effectiveness of Simulation Games. The article then proposes two theses intended to explain the potential causes of Simulation Game ineffectiveness: (1) the incompatibility of evaluation tools and (2) pedagogical deficiency inherent in Simulation Games.</p>

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)0.011
metaresearch head score (Gemma)0.036
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 score0.131
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.331
Teacher spread0.301 · 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