Guns in the North: Assessing the Impact of Social Identity on Firearms Advocacy in Canada
Bibliographic record
Abstract
Identity is an important aspect of group politics in Canada. This article examines the impact of gun owner’s social identity on the political participation of gun owners and, thus, the success of the Canadian gun rights movement. It investigates whether Canadian gun owners are politically active, and if so, why? The article is based on an online survey of 16,880 Canadian gun owners. Cross‐tabulation, probit regression, and negative binomial regression were used to assess the impact of gun owner’s social identity on political participation. Results indicate that gun owners are avid political participants and that this can be explained by the existence of a strong gun owner’s social identity within a subset of Canadians. This has implications for our understanding of how social identities tied to serious leisure communities can impact politics. 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): 827‐850. https://doi.org/10.1111/polp.12276 Smith‐Walter, Aaron, Holly L. Peterson, Michael D. Jones, and Ashley Nicole Reynolds Marshall. 2016. “ Gun Stories: How Evidence Shapes Firearm Policy in the United States.”Politics & Policy44 (6): 1053‐1088. https://doi.org/10.1111/polp.12187
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".