Morality Appraisals in Consumer Responsibilization
Bibliographic record
Abstract
Abstract In recent decades, U.S. gun rights lobbying groups, politicians, courts, and market actors have sought to responsibilize U.S. consumers to use firearms to address the societal problem of crime. These efforts center an interpretation of the constitutional right to keep and bear arms guaranteed by the Second Amendment as an entitlement for individuals to practice armed self-defense. Using interview and online discussion data, this research investigates consumers’ responses to responsibilization for this morally fraught set of behaviors, and the role of consumers’ various understandings of the right to bear arms in these responses. Findings show that consumers consider multiple, specific armed protection scenarios and accept responsibilization in only a portion of these scenarios while rejecting it for the remainder. Acceptance is determined by their appraisals of the morality of consumer responsibilization subprocesses. Consumers’ understanding of the constitutional right serves as a heuristic in these appraisals, with some understandings leading consumers to accept responsibilization across a much larger proportion of scenarios than others. Contributions include illustrating response to consumer responsibilization as a proportionality; illuminating consumers’ active role in appraising responsibilizing efforts; and demonstrating how some consumers come to understand a responsibilized behavior as a moral entitlement.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".