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Record W2154355261 · doi:10.1109/icsmc.2008.4811623

A visualized tool of role transfer

2008· article· en· W2154355261 on OpenAlexafffund
Haibin Zhu, Matthew Grenier, Rob Alkins, Jeffery Lacarte, Jordan Hoskins

Bibliographic record

VenueConference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 2008
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsNipissing University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTask (project management)Computer scienceDecision makerScheme (mathematics)Group decision-makingTask analysisKnowledge managementHuman–computer interactionTransfer (computing)Artificial intelligenceOperations researchEngineeringSystems engineeringPsychology

Abstract

fetched live from OpenAlex

When a crisis occurs, decision makers experience high tension and must make a decision in a short time. An automated tool with accurate analysis capability would help them make correct decisions. This paper presents a visualized tool to help a decision maker understand the structure of a group based on roles and agents (or people). The significant contribution of this tool is that it provides an exact solution to check if a group is workable, and if an agent (or a person) is critical for a group. It also suggests a role transfer scheme implementing time sharing mechanisms for when there are an insufficient number of agents (people) to complete a task simultaneously. Because role transfer is a fundamental problem in management, task assignment, and training, this tool can be used in many different ways, such as, training and management. It is also a direct assistance tool for crisis responses.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0430.006

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 head0.069
GPT teacher head0.326
Teacher spread0.257 · 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 designTheoretical or conceptual
Domainnot available
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

Citations5
Published2008
Admission routes2
Has abstractyes

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