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Record W2038843701 · doi:10.4043/20303-ms

When Failure is not an Option: Managing Megaprojects in the Current Environment

2009· article· en· W2038843701 on OpenAlexaff
Richard E. Westney, Joel Fort, James Lucas, Luc Messier, R. Don Vardeman, Kevin Renfro

Bibliographic record

VenueOffshore Technology Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsThursdayProject managementProcurementSession (web analytics)BusinessCorporationProject managerPublic relationsFinanceMarketingEconomicsManagementPolitical science

Abstract

fetched live from OpenAlex

Introduction This manuscript is intended to set the stage for the OTC General Session Panel discussion entitled " When Failure is not an Option: Managing Megaprojects in the Current Environment??, scheduled for Thursday, May 7, 2009. Members of this panel session are as follows:Richard Westney, Chairman, Westney Consulting Group (Panel Moderator)Joel Fort, General Manager, Yemen LNGJames Lucas, President & CEO, Luman InternationalLuc J. Messier, Senior Vice President - Project Development and Procurement, ConocoPhillipsDon Vardeman, Vice President -Worldwide Projects, Anadarko Petroleum Corporation Summary Megaprojects can be defined as projects that are so large that the conventional body of project and risk management knowledge is insufficient to ensure success. Given that significant overruns and delays on megaprojects are almost the norm, it appears that no one really has all the answers to meeting the special challenges of these huge projects. Failure is not an option for most megaprojects. The level of investment, the magnitude of the cashflows involved, and the organizational commitment required are such that the impact of bad outcomes can be devastating to the operator, partners and host countries involved. Megaprojects create challenges that, although now fairly typical, have not typically been addressed in the past. Given the relatively small number of past megaprojects, and their long duration, many executives and practitioners have limited or no experience with them - and the experience of those that do is often not the sort that one would wish to repeat. New approaches are needed, many have been tried, and a new body of knowledge is emerging for these very large projects. The panel is made up of people who are very experienced in megaprojects, representing the executive and project manager points of view, the owner and contractor points of view, as well as the independent perspective of consultants. Rather than focus on success stories, the panel will focus on the most difficult / challenging / intractable aspects of megaprojects and, with extensive audience participation, discuss what has worked, what has not, and what is needed going forward in the current energy and economic environment. Participants will take away an improved understanding that will assist in their planning and decision-making.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.600

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 science0.0010.000
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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2009
Admission routes1
Has abstractyes

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