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The Next Era of IPD Research: A Systematic Literature Review of The IPD Research Trends 2017-2020

2022· article· en· W4210369283 on OpenAlexaff
Ahmad J. Arar, Érik Poirier

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSystematic reviewFragmentation (computing)Management scienceData scienceEngineering ethicsComputer sciencePolitical scienceMEDLINEEngineering

Abstract

fetched live from OpenAlex

Abstract Questions around integration, innovation, and collaboration within project teams have been the focus of increasing research in recent years. This is in response to the notorious fragmentation and the inherent performance issues that face the construction industry. Among the solutions identified, new contractual approaches, namely, integrated project delivery (IPD), have been investigated to understand how they can help overcome the industry’s issues. This paper aims to contribute to the previous efforts that studied the research trends of the IPD from 2001 to 2016 by synthesizing the last four years from 2017 to 2020. This period accounts for more than 70% of research on IPD compared to the previous 16 years combined. A systematic literature review was conducted to understand the trends in IPD research over the last four years. The results allowed the research team to understand better where such research was being conducted, what type of projects were being studied, and most importantly, the topics being covered within this research and its results. This paper proposes an initial review that will be further developed in a full-length journal paper.

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)0.031
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.007
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
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 head0.202
GPT teacher head0.398
Teacher spread0.196 · 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.

Study designBench or experimental
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

Citations4
Published2022
Admission routes1
Has abstractyes

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