The differential impact of network connectedness and size on researchers’ productivity and influence
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
We analyze the effect of different types of online and offline ties – acquaintanceship, advice, and co-authorship – on researchers’ productivity and influence. Unlike static studies of networked work, we look at how changes in these networks affected researchers’ performance and influence. Using the number of publications as an indicator of productivity and the number of citations as an indicator of influence, we investigate when researchers were more productive and influential. We study whether their networks were cohesive, if the researchers were central in their networks or linked to central players, and whether their work had more opportunities to be disseminated through diverse, non-redundant ties. Although the connectedness of their networks was positively associated with the researchers’ productivity, it was the non-redundant effective size of the networks that was positively associated with the researchers’ influence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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