Gun rights groups set new lobbying spending record in 2021
Bibliographic record
Abstract
On Saturday, an 18-year-old gunman entered the Tops Friendly Supermarket in Buffalo, N.Y. He killed 10 people, injured three others and left a community reeling. Sen. Ted Cruz (R–Texas), who has received more funding from gun rights groups than any other politician since he was elected to Congress in 2012, condemned the racially-motivated mass shooting as "profoundly anti-American."But mass shootings are an increasingly common facet of American life. There have been 198 mass shootings in 132 days in 2022. The Buffalo massacre is the deadliest this year so far.Powerful gun rights groups including the National Rifle Association (NRA) and Gun Owners of America have poured millions into lobbying, campaign contributions and outside spending to advocate for the right to bear arms. At least 81.4 million Americans owned guns in 2021. Gun rights groups spent a record $15.8 million on lobbying in 2021 and $2 million in the first quarter of 2022. These organizations have invested $190 million in lobbying efforts since 1998. Gun rights advocates spent more than $114 million of that total since 2013.Lobbying by gun rights advocates nearly tripled in 2013 after a gunman murdered 26 people, including 20 children, at Sandy Hook Elementary on Dec. 14, 2012. The following year was the closest the Senate has come in the last decade to passing meaningful gun control legislation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.051 |
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".