MétaCan
Menu
Back to cohort
Record W4383957393 · doi:10.24928/2023/0235

How to Navigate the Dilemma of Value Delivery: A Value Identification Game

2023· article· en· W4383957393 on OpenAlexaff
Salam Khalife, Farook Hamzeh

Bibliographic record

VenueAnnual Conference of the International Group for Lean Construction · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversity of Alberta
FundersStanford Bio-X
KeywordsDilemmaValue (mathematics)Identification (biology)Computer scienceMathematics

Abstract

fetched live from OpenAlex

Delivering value on projects is one of the fundamental concepts in lean construction through the Transformation-Flow-Value (TFV) theory.The concepts of transformation and flow are thoroughly explained through the lean construction literature, and various educational games are offered to support the understanding of the flow concept including work-flow variability, takt time, waste elimination, pull systems, and efficient planning.The concept of value, however, tends to be more complicated where researchers are continuously trying to better understand value delivery on construction projects.The International Group for Lean Construction conference offered research on Target Value Design as well as games to reap knowledge about project value.This paper provides additional support to understand the value concept and its characteristics through a proposed educational simulation game.The game demonstrates how designers identify requirements on projects, how various parties value different things, and how to potentially deal with conflicting requirements.The game helps students and lean practitioners in understanding the process of eliciting perceived value on a project and achieving shared understanding through proper communication between different parties.This would help in managing projects in a way that delivers higher value for the different stakeholders, thus achieving successful projects with higher satisfaction rates.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.087
GPT teacher head0.347
Teacher spread0.260 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAnnual Conference of the International Group for Lean ConstructionSame topicTechnology Adoption and User BehaviourFrench-language works237,207