Institutions, interest groups, and gun control policy: A comparative study of focusing events
Bibliographic record
Abstract
This dissertation discusses the importance of firearms related focusing events in two states: the United States and Canada. It is my contention that focusing events can lead to items being placed on the agenda; however, interest group activity can either impede or promote the policy outcomes due to the event.;I argue that in the United States firearms related focusing events often lead to new policy being placed on the agenda when there is a Democratic government; however, due to the diffuse nature of the American political system and the presence of the NRA (a staunchly status quo group) policy rarely, if ever, goes to the formulation stage. Furthermore, when there is a Republican government in power, focusing events lead to fewer policy alternatives being placed on the agenda, much less advancing to the formulation stage.;Alternatively, in Canada, firearms related focusing events will lead to an item being placed on the agenda when there is a left of center government. Due to public outcry (from the masses and more importantly interest groups), and motivated policy entrepreneurs within government; policy makers will react quickly and decisively in creating new firearms legislation. There are two reasons for this: centralized power of the parliament, and the minimization of status quo interest group influence due to the blocking of alternative venues in the short term after a focusing event. This minimization of influence for these groups means that focusing events not only lead to new firearm legislation being placed on the agenda, but also allows other actors inside of government, in particular left of center policy entrepreneurs, to have significant power in policy making in the short term. When there is a right of center government, firearms related focusing events will lead to the policy being placed on the agenda, but rather than having major overhauls to the subsystem, changes will be incremental in nature, if they occur at all.
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 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 | 0.000 | 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) | 0.000 | 0.000 |
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 teacher head, 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".