Store robberies for tobacco products: Perceived causes and potential solutions
Bibliographic record
Abstract
Robberies of New Zealand convenience stores for tobacco products spiked between 2016 and 2017. According to media reports, many robberies involved the use of weapons and resulted in injury to retailers. We conducted a content analysis of all online media articles containing commentary about these robberies, published between 2014 and 2019, to identify the perceived causes of the increase in robberies for tobacco and remedies implemented or demanded. The commentators in the articles were categorized into three groups of stakeholders: elites, grassroots, and interest groups. Overall, there was a mismatch between perceiving the primary cause to be socially and economically determined and suggesting solutions that were mostly situational shop level changes or tertiary prevention strategies, such as more and harsher policing. A further mismatch was that existing policing policy was not adapted to balance the perverse consequences of the tobacco excise tax increases. Early commentators tended to deflect blame away from their own sector. Later commentary converged to agree that the high tobacco excise tax was a critical causal factor.
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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 | 0.001 | 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 | 0.001 | 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".