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Record W4383957469 · doi:10.24928/2023/0221

Development of an Immersive Virtual Reality Prototype to Explore the Social Mechanisms of the Last Planner® System

2023· article· en· W4383957469 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

VenueAnnual Conference of the International Group for Lean Construction · 2023
Typearticle
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVirtual realityPlannerHuman–computer interactionComputer scienceMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

A successful implementation of the Last Planner® System (LPS) requires not only education on its principles, but also managing social mechanisms it brings up to reach outstanding outcomes.Simulation games have been widely applied to teach LPS principles, but they do not seem to appropriately capture the social mechanisms due to lack of socio-technical realism and inadequate gaming controls (i.e., control external factors other than one of interest).Immersive Virtual Reality (IVR) technology has the potential to reveal the LPS's social mechanisms by providing a highly-controlled and realistic simulation environment.However, how to effectively leverage IVR for LPS simulation is not well understood.In order to bridge this gap, we identified the essential elements that an IVR simulation should have to study the LPS social mechanisms.We then developed and tested a multi-user IVR prototype with the identified elements to simulate the LPS use in a "hypothetical" construction scenario.The results show that the prototype is feasible for studying LPS's social mechanisms.This study lays a foundation for future research in using IVR simulation games to study LPS social mechanisms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.268
Teacher spread0.222 · 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