Quick Draw History - The NRA, The Politics of Memory & The Great Gun Debate
Bibliographic record
Abstract
It is widely acknowledged that the National Rifle Association (NRA) is an important actor in the American gun debate.While academic and popular writing on this topic often focuses on the NRA's lobbying and campaign donations, there is little focus on the group's mass mobilization efforts that make these formal political endeavors possible.This dissertation explores the questions: how has the NRA become such an influential collective actor?How can we understand the group's impact on firearms policy in the United States?More specifically, what role do narrative and memory play in understanding this influence?I argue that the NRA both draws upon and shapes historical macro-narratives regarding the role of firearms in America's and Americans' pasts as part of its larger effort to expand the gun culture, from which it draws its political support, and influence the gun debate, and thus firearms policy.These narratives are intended to reinforce the idea that firearms have played an integral part in American history, more so than in other countries, and that the United States has a historical tradition of gun ownership.This research is based on thematic analysis of NRA written and online audiovisual material, as well as three months of embedded fieldwork in Indiana and Virginia, which included participant observation at NRA events and firearm safety classes, an analysis of the NRA museum, and interviews with executives and ordinary members.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".