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Record W2062677517 · doi:10.1142/s0217595911003016

INTRODUCTION TO THE SPECIAL ISSUE ON "OPERATIONAL RESEARCH AND ASIA RISK MANAGEMENT"

2011· article· en· W2062677517 on OpenAlexaff
Desheng Wu, David L. Olson, Luis Seco, John R. Birge

Bibliographic record

VenueAsia Pacific Journal of Operational Research · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRisk managementOperational risk managementOperational riskRisk analysis (engineering)Management scienceComputer scienceBusinessEngineering

Abstract

fetched live from OpenAlex

Risks are traditionally defined as the combination of probability and severity, but are actually characterized by additional factors. We believe the characteristics of risks include uncertainties, dynamics, dependence, clusterings and complexities, which motivate the utilization of various operational research tools. The objective of this issue is to survey the practice of using operational research tools in risk management, especially Asian risk management.

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)0.004
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.053
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.005
Science and technology studies0.0020.002
Scholarly communication0.0070.007
Open science0.0020.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0530.024

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 head0.073
GPT teacher head0.333
Teacher spread0.260 · 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
GenreEditorial

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

Citations5
Published2011
Admission routes1
Has abstractyes

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