MétaCan
Menu
Back to cohort
Record W1593797138 · doi:10.5353/th_b4985873

Modeling and evaluating multi-stakeholder multi-objective decisions during public participation in major infrastructure and construction projects

2013· dissertation· en· W1593797138 on OpenAlexaboutno aff
Terry Li Hongyang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholderProcess (computing)Citizen journalismPublic participationBusinessProject stakeholderPlan (archaeology)Intervention (counseling)Process managementStakeholder analysisStakeholder engagementPublic relationsSalientManagement sciencePolitical scienceKnowledge managementEngineeringProject managementProject planningProject charterComputer sciencePsychology

Abstract

fetched live from OpenAlex

With a desire to increase the chance of success of major infrastructure and construction (MIC) projects, it is increasingly common to invite the public participating in the planning and design processes. Public participation requires the involvement of individuals and groups who are positively or negatively affected by a proposed intervention (e.g. a project, a program, a plan, a policy). An effective public participation program can be beneficial to the parties involved (i.e. the decision makers and general public) in many ways. As a result, a number of participatory approaches have been developed by various sectors to drive the process of agenda-setting, decision-making, and policy-forming. Many research studies focusing on various aspects of participation in policy-making in general have been conducted, but few have looked into its application in the construction and infrastructure industry in particular. On the other hand, the decision making process of contemporary MIC projects is becoming ever more complicated especially with the increasing number of stakeholders involved and their growing tendency to defend their own interests. Failing to address and meet the concerns and expectations of stakeholders may result in project failures. To avoid this necessitates a systematic participatory approach to facilitate the decision making and evaluation. This research, therefore, aims to develop a multi-stakeholder multi-objective decision making and evaluation model to help resulting in consensus and increasing the satisfaction among various stakeholders (or stakeholder groups) in MIC projects. In this research, an extensive literature review was first carried out to examine the salient elements of public participation in MIC projects and to identify the barriers to effective public participation in project decision making in different countries (e.g. Australia, Canada, United Kingdom, United States, South Africa, etc.). China being a developing country was selected for in-depth case study analysis. Through a series of interviews, the underlying reasons for ineffective participatory practice in China were revealed. A questionnaire survey was then conducted to unveil those stakeholder concerns pertinent to MIC projects at the conceptual stages through the degree of consensus and/or conflict involved. Finally, a multi-stakeholder multi-objective decision model and a multi-factor hierarchical comprehensive evaluation model were developed. These two models were founded on the decision rule approach and the fuzzy techniques respectively. Another round of interview was conducted to investigate the (i) influence of different stakeholder groups in making decisions related to MIC projects during their conceptual stages; and (ii) relationship between the satisfaction of a single stakeholder group and that of the stakeholders overall. The application of the two models was demonstrated by two cases in Hong Kong and their validity was confirmed through validation interviews. The results indicated that the two models are objective, reliable and practical enough to cope with real world problems. The research findings are therefore valuable to the government and construction industry at large for successful implementation of public participation in MIC schemes locally and internationally in future, especially when the construction industry is becoming increasingly globalized and the trend of cultural integration between the East and West is ever growing.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.233
GPT teacher head0.420
Teacher spread0.186 · 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 designOther design
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
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicConstruction Project Management and PerformanceFrench-language works237,207