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Record W4394714036 · doi:10.1111/dsji.12315

Teaching information flow in supply chains: A role‐playing game using <i>TagScan</i>

2024· article· en· W4394714036 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDecision Sciences Journal of Innovative Education · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsMacEwan University
Fundersnot available
KeywordsComputer scienceSupply chainFlow (mathematics)Supply chain managementMathematics educationMultimediaKnowledge managementBusinessPsychologyMarketingMathematics

Abstract

fetched live from OpenAlex

Abstract Information flow is one of the three main flows of supply chains. It is an abstract concept that can be challenging for students to grasp in its entirety. This article describes a role‐playing game for teaching the topic of information flow in an undergraduate supply chain management course. The game allows students to simulate receiving and fulfilling customer orders by playing five roles within a manufacturing company. Students use TagScan , an augmented reality barcoding and logistics system launched by a technology company in western Canada, to track information throughout the game. Pre‐ and postsurvey results demonstrate the effectiveness of the proposed game in helping students visualize abstract course concepts and understand the types of information being tracked, the available information transmission technology, and the dynamics of information flow in a supply chain. Students were actively engaged in this in‐class activity and responded positively to the learning‐by‐gaming experience.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.012
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 head0.040
GPT teacher head0.373
Teacher spread0.333 · 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