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Record W2254606884 · doi:10.3963/jmpm.v3i3.167

3D Boundary Objects in Stakeholder Management: Knowledge Creators for the Project and Collaboration Facilitators

2016· article· en· W2254606884 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 Modern Project Management · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsKnowledge managementPraxeologyStakeholder engagementStakeholderProject stakeholderContext (archaeology)Citizen journalismStakeholder analysisProject managementBoundary (topology)Stakeholder managementProject management triangleProcess managementProject charterComputer scienceBusinessEngineeringPolitical sciencePublic relationsEpistemologyWorld Wide WebSystems engineeringGeography

Abstract

fetched live from OpenAlex

The study presented in this paper deals with the use of 3D boundary objects in stakeholder management. The main research objective is to understand the contribution that 3D boundary objects can make to a project in terms of knowledge and stakeholder engagement. The methodology used here is of a participatory and collaborative nature, and this choice is tied to the praxeological and theoretical context of the study. The results of the study show that 3D boundary objects facilitate the engagement of stakeholders and create knowledge in certain conditions, in particular related to the management style of the project manager, his or her experience and expertise. The praxeological and theoretical implications encompass both learning for a practitioner and the pertinence of enriching certain project management conceptualizations.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.103
GPT teacher head0.372
Teacher spread0.269 · 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