Some Challenges in the Empirics of the Effects of Networks
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
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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