3D Boundary Objects in Stakeholder Management: Knowledge Creators for the Project and Collaboration Facilitators
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".