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Record W2810491014 · doi:10.1177/875697280103200204

The Dynamic Baseline Model for Project Management

2001· article· en· W2810491014 on OpenAlex
Mark A. Seely, Quang P. Duong

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

VenueProject Management Journal · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBaseline (sea)Project managementEarned value managementProject management triangleComputer scienceAsk priceProcess (computing)Process managementProject planningExtreme project managementMatching (statistics)Engineering managementEngineeringSystems engineeringProject charterOPM3BusinessMathematics

Abstract

fetched live from OpenAlex

This paper describes the Dynamic Baseline Model © (DBM) as a framework for analysis of the project management learning process and an indicator of the expected success of a project. By matching project complexity with the appropriate project management approach, the DBM identifies individual learning needs and the appropriate response to the challenges of today's projects. As project management tools and techniques are more and more applied as a one-size-fits-all solution, there is a need to explore beyond these tools and techniques. The DBM suggests that our ability to create solutions is bounded by our current learning horizon, which may be too restrictive for the needs of a project. The model helps us find suitable solutions by enabling us to ask the right questions.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
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 head0.103
GPT teacher head0.401
Teacher spread0.298 · 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