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Record W2102126059 · doi:10.3329/jme.v45i1.24385

DEVELOPING GROUP LEADERSHIP AND COMMUNICATION SKILLS FOR MONITORING EVM IN PROJECT MANAGEMENT

2015· article· en· W2102126059 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

VenueJournal of Mechanical Engineering · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEarned value managementScheduleScope (computer science)Project managementWork (physics)Project management triangleWarning systemProject planningProcess managementComputer scienceSenior managementEngineering managementProject charterOperations managementBusinessEngineeringSystems engineeringTelecommunicationsPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Earned Value Management (EVM) is used to track the progress and status of a project with forecasting future performance. Leaders use the Evaluation, Verification and Monitoring with Earned Value Management (EVM) technique to evaluate their project progress and performance as an ‘early warning tool’. Monitoring EVM involves determining whether the project is on, ahead of, or behind schedule and on, under or over budget. Usually an organization has many people with many multi-dimensional strategic ideas. All of these people are focused on ultimate end points, they work on the same target, they play on the same field, and they gain their knowledge about the same markets and share their ideas with personal confident. They rely on EVM which integrates cost, schedule and scope to capture project progress assessment and project completion update.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.211
GPT teacher head0.380
Teacher spread0.170 · 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