How to Navigate the Dilemma of Value Delivery: A Value Identification Game
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".