The Impact of Brokerage in a Communication Network on Productivity: Evidence from Sensor Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Problem-solving effectiveness is key to organizational performance. To solve problems, gathering information from colleagues is critical, and positioning brokerage in communication networks is beneficial. The communication network for problem-solving is formed depending on the nature of the problem. Thus, the problem-solving network is the relational event network, and the connection of the problem-solving network dynamically changes over time depending on the problem basis. This study investigates the dynamics of brokerage in a problem-solving network and its impact on productivity in a company that provides technical support and troubleshooting for the IT system that its corporate customers use. By exploiting high-frequency data on face-to-face communication among employees collected by wearable sensors, we established the following results. First, the communication partners of each employee change weekly, which is a reasonable time to solve problems in the company. Second, with the change in the communication network, employees who position brokerage also change on a weekly basis. Third, while brokerage in a week has a positive impact on employee performance during the week, it has no impact on employee performance in the following week.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it