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Megaprojects

2020· reference-entry· en· W4247730065 on OpenAlexaff
Shankar Sankaran, Daphne Freeder, Alexandra Pitsis, Stewart Clegg, Nathalie Drouin, Marie‐Andrée Caron

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMegaprojectScope (computer science)Function (biology)Scale (ratio)BusinessProject planningProject managementEngineeringComputer scienceGeographySystems engineering

Abstract

fetched live from OpenAlex

“Megaprojects” is a term used to refer to projects and events that encompass large-scale projects in size, cost, space, time, energy, and influence. They are synonymous with large engineering projects, complex projects, large transport or energy projects, and large infrastructure projects, and are often composed of multilayered discrete projects forming a larger scale complex project. Some of the complexity deals with difficulty in quantifying the long-terms costs or benefits or fully realising the whole life cycle of the megaproject prior to commencement. Megaprojects are often shaped by contextual factors. Where complexity is related to technical aspects of the project it also includes organizational aspects and the scope of the project. Some of these projects are multifaceted and relate to science research, engineering infrastructure, or private and public construction of buildings and/or other venues. Megaprojects affect societies that undertake them, urban planning aspects, and social relationships between stakeholders engaged in executing all the elements involved in creating them. They have an impact on a number of areas both locally and globally. This includes extending notions of urban planning to accommodate large-scale construction. These projects can be significant in terms of social and/or economic factors in a positive or negative sense. There have been debates and criticism on the need and function of megaprojects and whether they are beneficial constructs or detrimental to society.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.398
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0220.012

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 head0.193
GPT teacher head0.389
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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