Law and Finance: Inaccurate, Incomplete, and Important
Bibliographic record
Abstract
but we find it convincing.And, in response to some of their critics, La Porta, Lopez-de-Silanes, and Shleifer have rectified some of their strong and prescriptive former claims.4 In this Essay, we discuss in Part II what we have learned from this agitated debate with so many policy and real life ramifications, why it is important to conceptualize a larger and more complex picture of the proposed law-finance causality, what we can learn from existing research on comparative systems in social science, and what we need to study next in the field of research of legal systems and economic sociology.In Part III, we discuss why it is important to get "law and finance" right.II.
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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 | 0.043 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.024 | 0.054 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".