Knowledge Integration in Complex Collaboration
Bibliographic record
Abstract
Multidisciplinary knowledge teams are often used in organizations because most innovation happens at the boundaries between disciplines or specializations. When confronted with novel and uncertain tasks, team members have to evaluate others’ expertise and knowledge contributions and integrate disparate knowledge. On one hand, they can emphasize knowledge validation, elaboration, and explicitly confront the differences found across knowledge boundaries – an approach referred to as knowledge traversal. On the other hand, they can trust each other’s domain expertise, avoid knowledge conflict, and co-create a common framework based on a meshing of individual contributions – an approach referred to as knowledge transcendence. However, little is known about why team members engage in either practice, what situations trigger them, and when it is necessary to accept or confront the knowledge of the other team members during complex collaboration. This research identifies how these knowledge workers assess when they need to engage in deep dialogue (i.e., knowledge traversal) and when they accept each other’s knowledge contribution (i.e., knowledge transcendence). Based on in depth interviews with 27 knowledge workers involved in complex tasks in multidisciplinary settings, we offer a practice-based view how knowledge integration is achieved.
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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".