Is a Fine Still a Price? Replication as Robustness in Empirical Legal Studies – Data and Supporting Materials, 2019
Bibliographic record
Abstract
This data set includes data and materials used to test the effect of fines on compliance behaviour by individuals. The data were generated by having our participants complete surveys that experimentally varied fine conditions in two settings: a daycare vignette asking parents about late pick up of children and a tax reporting scenario asking participants about fully reporting income. The data were generated as part of a project seeking to replicate the result in Uri Gneezy & Aldo Rustichini, "A Fine is a Price" (2000), XXIX Journal of Legal Studies 1. This well known study produced the surprising result that imposing a fine could increase non-compliance by individuals. Our data and analysis were generated to test the robustness of this finding using an alternative methodology and multiple settings. Details of analysis and results can be found in our article, Cherie Metcalf, Emily A. Satterthwaite, J. Shahar Dillbary, Brock Stoddard, “Is a Fine Still a Price? Replication as Robustness in Empirical Legal Studies” (2020) 63 International Review of Law & Economics, SI: Empirical Legal Studies Replication Conference, 2019 https://doi.org/10.1016/j.irle.2020.105906
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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 | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.047 |
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".