Taking stock: The contribution of policy studies to our understanding of gun policy
Bibliographic record
Abstract
Abstract In the summer of 2022, the tragic mass shooting in Uvalde, Texas once again thrust firearms policy onto the public agenda and opened an agenda window for bipartisan gun reforms in the Senate. Almost simultaneously, the Supreme Court struck down restrictions on concealed carry in New York state in the first major ruling on Second Amendment Rights sinceMacDonald v.Chicago. Given the salience of firearms policy to contemporary policy debates, it is important to take stock of how the discipline of policy studies has contributed to our understanding of this pressing area of policy. As we see, scholars of policy studies have largely left discussions of firearms policy to disciplines like criminology, sociology, economics, and public health. Despite this, a small group of scholars has used the theoretical toolkit of our discipline to better understand this issue. This short review article will present the contribution of policy studies to our understanding of firearms policy from Sandy Hook to Uvalde and provide suggestions for areas where the discipline can contribute to our understanding of this vitally important regulatory issue. Most importantly, more work is needed to develop robust and multidimensional evaluations of firearms policies. Related Articles Cook, Philip J., and Jens Ludwig. 2019. “Understanding Gun Violence: Public Health vs. Public Policy.”Journal of Policy Analysis and Management38(3): 788–95. https://doi.org/10.1002/pam.22141 . Haider‐Markel, Donald P., and Mark R. Joselyn. 2017. “Special Issue Editors' Introduction: Gun Politics.”Social Science Quarterly98(2): 377–81. https://doi.org/10.1111/ssqu.12415 .
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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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".