Bibliographic record
Abstract
Abstract The mass shootings in Buffalo, New York, and Uvalde, Texas in May 2022 prompted Congress to enact the first significant federal gun legislation since the 1990s. While many commentators have framed this policy change as a remarkable break from the long‐standing pattern of inaction on gun violence, I argue that political actors perceived and responded to the problem in familiar ways. Drawing on agenda setting and information processing theories, I highlight factors that suggest no fundamental alteration in how the U.S. political system responds to gun injury and death. I also point to changes in public opinion and in the interest group landscape that have the potential (in the long term) to transform the politics of gun policy. Finally, I conclude with some near‐term expectations for policy making and its effects on the issue. Related Articles Cagle, M. Christine, and J. Michael Martinez. 2004. “Have Gun, Will Travel: The Dispute between the CDC and the NRA on Firearm Violence as a Public Health Problem.”Politics & Policy32(2): 278–310. https://doi.org/10.1111/j.1747‐1346.2004.tb00185.x . Joslyn, Mark R., and Donald P. Haider‐Markel. 2018. “Motivated Innumeracy: Estimating the Size of the Gun Owner Population and its Consequences for Opposition to Gun Restrictions.”Politics & Policy46(6): 827–50. https://doi.org/10.1111/polp.12276 . Schwartz, Noah S. 2021. “Guns in the North: Assessing the Impact of Social Identity on Firearms Advocacy in Canada.”Politics & Policy49(3): 715–818. https://doi.org/10.1111/polp.12412 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".