Collaborative Meeting as an Integrative Mechanism in a Multinational Investment Project
Bibliographic record
Abstract
In multinational and complex projects that are often implemented by multiple organizations, the entire projects need to be divided into manageable subprojects. At the same time, all subprojects are needed to be kept aligned with the project goals and targets by integration and coordination. The purpose of this article is to study the role of a particular, work-shop type, collaborative meeting by utilizing the characteristics of an integrative information processing framework. A single case study method was used to observe the practices of collaborative meetings. This study contributes to the project management research by analysing how collaborative meeting practice can be used as a mechanism to reduce uncertainty and equivocality in a large investment project. The results of this study are two folds: Firstly, the case project’s collaborative meetings are described in detail; secondly, the perceived features and procedures of the collaborative meetings in the case project are illustrated showing the role of the collaborative meetings as an integrative tool. Moreover, the perceived integrative characteristics of the collaborative meetings reducing uncertainty and equivocality are presented. This study indicates that collaborative meeting is an integrative mechanism reducing uncertainty and equivocality in a large investment project context.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".