Gun rights groups set new lobbying spending record in 2021
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.098 | 0.012 |
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 it