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Record W2026202047 · doi:10.1016/j.jom.2004.05.002

Empirical simulation studies in operations management: context, trends, and research opportunities

2004· article· en· W2026202047 on OpenAlexaff
Scott M. Shafer, Timothy L. Smunt

Bibliographic record

VenueJournal of Operations Management · 2004
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsEmpirical researchContext (archaeology)Computer scienceOperations researchPeriod (music)Operations managementManagement scienceStatisticsHistoryEconomicsMathematics

Abstract

fetched live from OpenAlex

Abstract This study investigates, published simulation studies in operations management (OM) that are empirically based. The results of the study are based on an exhaustive search of twenty leading operations management journals over the period from 1970 to 2000. Approximately, 600 published simulation studies in operations management were identified, but only 85 of these were subsequently identified as being empirical in nature. The 85 articles were next classified into one of 17 categories. Results by journal, topic, time period, and combinations of these factors are reported. Finally, opportunities for future research are discussed.

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)0.036
metaresearch head score (Gemma)0.147
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.147
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.015
Science and technology studies0.0000.002
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.750
GPT teacher head0.593
Teacher spread0.157 · 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.

Study designSimulation or modeling
DomainMethods
GenreEmpirical

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

Citations76
Published2004
Admission routes1
Has abstractyes

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