Some Challenges in the Empirics of the Effects of Networks
Bibliographic record
Abstract
This chapter studies some recent developments and challenges in the empirics of the effects of social networks. The authors focus in particular on researchers’ ability to make policy recommendations based on a standard linear econometric model. The chapter examines the potential compatibility between this type of econometric model and a microeconomic theoretical approach based on fundamentals, such as preferences, technology, and decision processes. The chapter discusses sources of identification for the social multiplier as well as for the identity of the key player. The authors study the possibility of testing endogeneity in network formation. The chapter analyzes the use of proxy variables and their impact for the causal interpretation of peer effect coefficients. This analysis suggests that greater care should be taken in grounding econometric network models to sound and credible theoretical underpinnings.
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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 | 0.038 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".