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Record W4385478623 · doi:10.37256/ccds.5120233284

Decision Making: Models, Processes, Techniques

2023· article· en· W4385478623 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.

Bibliographic record

VenueCloud Computing and Data Science · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsResearch & Development CorporationUniversity Canada West
Fundersnot available
KeywordsBusiness decision mappingProcess (computing)Management scienceDecision engineeringComputer scienceDecision-makingDecision analysisR-CASTKnowledge managementDecision processDecision support systemArtificial intelligenceBusinessEngineeringMarketingEconomics

Abstract

fetched live from OpenAlex

Decision-making is one of the steps in problem-solving that can be applied in manifold areas from personal situations to the management of organizations. There are functions and processes to lead to making a decision; however, it may sound complicated to select between decision-making models and approaches as different factors and different outcomes get involved in the decision-making process. This article is a survey of decision-making with managerial insight to explain what it is, what kinds of decisions are made, and how they are applied in many sectors, including computers, management, business, psychology, etc. This paper aims to provide an overview of the decision-making concept, its functions, process steps, and its main types, models, and categories. Overall, it provides valuable insights for individuals and organizations seeking to improve their decision-making abilities.

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)0.002
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 score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0020.004
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 head0.140
GPT teacher head0.366
Teacher spread0.227 · 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