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Record W1831027016 · doi:10.1111/dsji.12077

Plan Before You Play: An Activity for Teaching The Managerial Process

2015· article· en· W1831027016 on OpenAlexaff
Norm Althouse, Peggy L. Hedges

Bibliographic record

VenueDecision Sciences Journal of Innovative Education · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCreativityPlan (archaeology)Competition (biology)Process (computing)Class (philosophy)Subject (documents)Computer scienceInvestment (military)Mathematics educationKnowledge managementTeaching methodPsychologyWorld Wide WebArtificial intelligenceSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT This article describes a 60‐minute classroom activity using LEGO® bricks that demonstrates and reinforces the importance of the managerial process. The activity, Plan Before You Play (PBP), is targeted to introductory business classes, and differs from others in that it requires little investment or up‐front planning, is easily scalable, and, with a few minor adjustments, can be altered sufficiently to discourage information drift across multiple class offerings and academic terms. It can also be modified to subject specific discussions. Students can participate in PBP regardless of their level of knowledge or interest in the subject. Participating in PBP provides students with a concrete example of how good communication and a decision framework can result in more efficient use of resources. The activity also encourages creativity and can be used to generate healthy competition between student groups.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.007

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 head0.062
GPT teacher head0.377
Teacher spread0.315 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2015
Admission routes1
Has abstractyes

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