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10.5.0 Panel 10.5.0: Cultural, Psychological and Motivational Factors in Risk Management: “<i>Major Issues</i>” or “<i>Let's Not Go There</i>”

2007· article· en· W1994861321 on OpenAlexaff
Jack Stein, A. Dolan, Tom Gilb, Sally Jackson, Garry Roedler, William T. Siefert

Bibliographic record

VenueINCOSE International Symposium · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsLockheed Martin (Canada)University of Toronto
FundersUniversity of Missouri
KeywordsAcknowledgementFeelingWorryRisk perceptionRisk managementPerceptionPsychologyPublic relationsSocial psychologyBusinessPolitical scienceComputer scienceComputer securityPsychiatry

Abstract

fetched live from OpenAlex

Abstract There is abundant evidence that the biggest obstacles to effective risk management are psychological paradigms rooted in the organization's culture. These paradigms were present in the Challenger, Columbia and Chernobyl catastrophes. Consider the following examples: When asked to name their top risks, program decision‐makers confidently do so. When asked if the risks are in the database, a typical response is “no, that's not the type of thing we put in our risk management database.” Some individuals view risks as challenges; others fear their acknowledgement. Many engineers deny or don't communicate risks. Program managers wanting to present their program in a positive manner to management or the customer often downplay serious risks. In the Time Magazine article “Why We Worry About the Wrong Things – The Psychology of Risk” psychology professor Paul Slovic explains, “there are two systems for analyzing risk: an automatic intuitive system and a more thoughtful analysis. Our perception of risk lives largely in our feelings, and most of the time we're operating on this system.” In evaluating a risk, the brain's most primitive part, the amygdala, acts first. It is not till later that the higher regions of the brain get the signal. This, explains Time author Jeff Kluger, is why “… we wring our hands over the mad cow pathogen that might be (but almost certainly isn't) in our hamburger, and worry far less about the cholesterol contributing to heart disease that kills 700,000 of us annually.” Intelligent Enterprises effectively employ risk management processes and tools to reinforce utilization of the higher regions of the brain over the primitive part. Our panelists are among the world's most highly respected SE and Risk Management practitioners. They will characterize psychological, cultural and motivational inhibitors and describe key practices used by Intelligent Enterprises to accomplish Intelligent Risk Management. Further information about this panel topic, and risk management in general panel, may be found at http://www.incose.org/practice/techactivities/wg/risk/ .

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.048
GPT teacher head0.362
Teacher spread0.314 · 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
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

Citations0
Published2007
Admission routes1
Has abstractyes

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