Using TVD Simulation to Improve Collaboration
Bibliographic record
Abstract
Target Value Design (TVD), a lean approach, has been implemented successfully in the past decade in various countries and its process mandates the collaboration of project participants.However, issues of adapting collaborative practices and the time it takes firsttime users to understand TVD practices have been a challenge in TVD projects.Recently, there has been an increase in the creation, reinvention and use of simulations and serious games to teach TVD and other lean principles to project stakeholders encountering them for the first time.The 50 minute version of the simulation game developed in Texas A & M University was used to illustrate TVD practice and collaboration in this study.The study used 24 industry stakeholders from a reputable real estate developer during the implementation of TVD on a live project in Nigeria.The results reported that the simulation is effective in illustrating the practices of TVD including collaboration and designing to set targets.Finally, this study recommends the inclusion of the TVD simulation game in training and workshops for project team before the commencement of construction projects because it demonstrated to be a simple and practical method of understanding collaboration and TVD practices.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".