Information Brokers in Requirement-Dependency Social Networks
Bibliographic record
Abstract
Requirements interdependencies create technical dependencies among project members that generally belong to different functional groups in an organization, but who need to coordinate activities during processes of requirements change management. Effective knowledge management is needed to disseminate information on requirement changes across teams working on interdependent requirements to avoid mis-interpretations. Social networks are regarded as important in fostering knowledge management, where brokers or gatekeepers have the role of project members facilitating information flow. However, little is known about processes of information flow and brokerage in social networks built around interdependent requirements. In a field study of requirement interdependencies in a large IT manufacturing organization, we found that brokers holding pockets of knowledge have an impact on information flow in requirement-interdependent teams. We discuss a number of patterns of information flow and draw implications for processes of requirements change management.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".