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Attitude-Based Negotiation Methodology for the Management of Construction Disputes

2010· article· en· W2098088157 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management in Engineering · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNegotiationConflict resolutionManagement scienceComputer scienceResolution (logic)Process managementOperations researchKnowledge managementBusinessEngineeringPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A systematic negotiation methodology for construction disputes is presented to take into consideration the attitudes of negotiators at two complementary levels of decision making: strategic and tactical. At the strategic level, the proposed methodology employs the graph model for conflict resolution and helps negotiators find the most beneficial subset of solutions to the conflict. At the tactical level, the proposed methodology examines the most beneficial strategic decisions using utility functions to provide agreed-upon tradeoffs with respect to any conflicting issues. A construction case study is used to illustrate the proposed methodology and demonstrate the importance of incorporating decision makers’ attitudes into negotiation to better identify the most feasible decisions. The proposed methodology may assist negotiators with the challenges of conventional negotiation through the incorporation of decision makers’ attitudes into a range of analytical tools that will clarify interests, determine equilibrium outcomes, identify tradeoffs, recognize negotiators’ satisfaction, and generate optimum solutions.

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.

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)0.005
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: Methods · Consensus signal: none
Teacher disagreement score0.658
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.095
GPT teacher head0.372
Teacher spread0.278 · 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