Enhancing Shared Understanding in Multidisciplinary Teams
Bibliographic record
Abstract
Abstract Modern complex projects increasingly rely on multidisciplinary teams, where members from diverse disciplines collaborate to address intricate challenges. Achieving a shared understanding and alignment in such teams remains an obstacle. This study conducted by INCOSE Technical Leadership Institute (TLI) Cohort 9 explores the role of systems engineering in facilitating shared understanding within multidisciplinary teams. The research employed a mixed‐methods approach which included a literature review, interviewing industry and INCOSE leaders, and hosting interactive workshops. It identified five key challenges hindering shared understanding: communication breakdowns, organizational conflict, unconscious bias, microaggressions, and a lack of inclusive policies. To address these, the study proposed implementing strategies such as structured communication frameworks, early stakeholder involvement, bias awareness, and inclusive workplace policies. The findings highlight systems engineering as a bridge of effective collaboration, bridging gaps across disciplines and promoting alignment. While the study offers insight, it also identifies the need for application into real world environments, further research on metrics of shared understanding, validation techniques, and leveraging collaboration tools. This research advances the understanding of multidisciplinary teams and provides practical strategies for fostering collaboration and achieving shared goals in complex and global environments.
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 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 | 0.030 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".