The Company You Keep: How Network Disciplinary Diversity Enhances the Productivity of Researchers
Bibliographic record
Abstract
The Covid-19 pandemic has affected most organizations’ working environment and productivity. Organizations have had to make arrangements for staff to operate remotely following the implementation of lockdown regulations around the world, as the pandemic has led to restrictions on movement and the temporary closure of workplace premises. The purpose of this paper is to gain a deeper understanding of the effect of this transition on productivity during the pandemic, by studying a distributed network of research who collaborate remotely. We examine how the productivity of researchers is affected by the distributed collaborative networks in which they are embedded. Our goal is to understand the effects of brokerage and closure on the researchers’ publication rate, which is interpreted as an indicator of their productivity. We analyze researchers’ communication networks, focusing on structural holes and diversity. We take into account the individual qualities of the focal researcher such as seniority. We find that disciplinary diversity among researchers’ peers increases the researchers’ productivity, lending support to the brokerage argument. In addition, we find support for two statistical interaction effects. First, structural holes moderate diversity so that researchers with diverse networks are more productive when their networks also have a less redundant structure. Diversity and structural holes, when combined, further researchers’ productivity. Second, seniority moderates diversity such that senior researchers are more productive than junior researchers in less diverse networks. In more diverse networks, junior researchers perform as well as senior researchers. Social capital and human capital are complementary. We conclude that the benefits of diversity on researchers’ productivity are contingent on the qualities of the researchers and on network structure. The brokerage/closure debate thus needs a more nuanced understanding of causal relationships.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".