Optimizing Network Communication Structure for Knowledge Transmission in Organizations
Bibliographic record
Abstract
Organizational stakeholders are often burdened with the amount of information coming their way through various means of organizational communication. Information overload has been studied, and methodologies have been proposed to optimize the amount of information to which each user is exposed to be consistent with their processing capacity, mostly through filtering approaches. Our focus is on investigating the impact of communication network topologies that maximize the overall network value. We propose a conceptual framework, followed by a mathematical simulation model, of a small network of uniform users with limited information processing capacities, exchanging messages that decay over time at a uniform, organization-wide rate. Exogenous concepts in our framework are the amount of information generated in an organizational network which is then presented to individual stakeholders, the stakeholder’s ability to process information and the organization-wide information decay factor. Within the context of our proposed framework, we investigate the ability of different network topologies to facilitate the dissemination of organizational information. The level of interconnectedness of the network is expressed via different graph metrics with the minimum inbound degree being the most critical indicator of network success within the context of our benchmark organizational model across a variety of scenarios representing different levels of information decay and the stakeholders’ ability to consistently contribute information of value to other stakeholders. Our results suggest that organizations should strive for levels of interconnectedness in their communication networks that are consistent with the stakeholders’ information processing capacity.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".