Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs
Bibliographic record
Abstract
Actors and associates often match on a few dimensions that matter most for the relationship at hand. In so doing, they are exposed to unanticipated social influences because counterparts have broader attitudes and preferences than would-be contacts considered when they first chose to pair. The authors label as “partially deliberate” social matching that occurs on a small set of attributes, and they present empirical methods for identifying causal social influence effects when relationships follow this generative logic. A data set tracking the training and professional activities of academic biomedical scientists is used to show that young scientists adopt their advisers’ orientations toward commercial science as evidenced by adviser-to-advisee transmission of patenting behavior. The authors demonstrate this in two-stage models that account for the endogeneity of matching, using both inverse probability of treatment weights and an instrumental variables approach. They also draw on qualitative methods to support a causal interpretation. Overall, they present a theory and a triangulation of methods to establish evidence of social influence when tie formation is partially deliberate.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".