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Record W4220776861 · doi:10.1061/9780784483978.053

Developing a Value Dashboard for Tracking Value Alignment during Design

2022· article· en· W4220776861 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

VenueConstruction Research Congress 2022 · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicValue Engineering and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDashboardValue (mathematics)Computer scienceTracking (education)Software engineeringMachine learningSociology

Abstract

fetched live from OpenAlex

Delivering value to the customer and to the internal and external stakeholders on construction projects is deemed essential for projects’ success. However, literature is limited in reference to the methods offered to track value alignment on projects. Defining and agreeing on what constitute project value early on would help in providing a clear starting point to capture value generation along project phases. Accordingly, this research advises first on the main steps to evaluate and classify project value, then proposes a design for a value dashboard (VDB) to visualize and track value on projects. The suggested dashboard acts as a visual management tool to guide project managers with the decision-making process considering the evolving understanding of value. The advocated value dashboard for the design phase is discussed and vetted with experts from the construction industry. Future research will further test the VDB on real life projects to demonstrates its applicability. This visual dashboard is a novel tool that provides tracking for value generation and delivery, and it is complementary to the existing project management dashboards that focus on schedule and cost considerations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.087
GPT teacher head0.318
Teacher spread0.231 · 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