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Record W2117842831 · doi:10.1139/l09-101

Project success indicators focusing on residential projects: Are schedule performance index and cost performance index accurate measures in earned value?

2009· article· en· W2117842831 on OpenAlexvenueno aff
Sang‐Chul Kim

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEarned value managementScheduleIndex (typography)Value (mathematics)Meaning (existential)Control (management)Operations researchMeasure (data warehouse)Computer scienceCost estimateWork (physics)Operations managementProject managementEngineeringProject planningSystems engineeringData miningArtificial intelligence

Abstract

fetched live from OpenAlex

As a concept of project control that provides a quantitative measure of schedule and cost information, earned value (EV) can evaluate work progress by identifying potential delays and cost overruns. When EV is used as a construction control technique, the schedule performance index (SPI) and the cost performance index (CPI) are the core factors in the EV system. The standard is only 1, i.e., 1.0 above or below. Though the two indices have important roles in the EV system, those indices do always show the project status exactly; for example, when planned value (PV), EV, and actual cost (AC) are not collected in a timely manner. Additionally, there is no guideline on how each value in SPI and CPI is understood or how to read the hidden meaning behind the construction status. Therefore, this study first speculates about the meaning of the two indices, then suggests a practical application, and finally shows the framework where the two indices apply to various types of construction.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.291
Teacher spread0.247 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2009
Admission routes1
Has abstractyes

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