Guardians and handlers: the role of bar staff in preventing and managing aggression
Bibliographic record
Abstract
AIMS: To identify good and bad behaviors by bar staff in aggressive incidents, the extent these behaviors apparently reflect aggressive intent, and the association of aggressive staff behavior with level of aggression by patrons. DESIGN, SETTING AND PARTICIPANTS: Data on staff behavior in incidents of aggression were collected by 148 trained observers in bars and clubs on Friday and Saturday night between midnight and 2 a.m. in Toronto, Canada. Behaviors of 809 staff involved in 417 incidents at 74 different bars/clubs were analysed using descriptive statistics and three-level hierarchical linear modeling (HLM) analyses. MEASUREMENTS: Observers' ratings of 28 staff behaviors were used to construct two scales that measured escalating/aggressive aspects of staff behavior. Apparent intent level for bar staff was dichotomized into (1) no aggressive intent versus (2) probable or definite aggressive intent. Five levels of patron aggression were defined: no aggression, non-physical, minor physical, moderate physical and severe physical. FINDINGS: The most common aggressive behaviors of staff were identified. Staff were most aggressive when patrons were either non-aggressive or highly aggressive and staff were least aggressive when patrons exhibited non-physical aggression or minor physical aggression. Taking apparent intent into consideration decreased staff aggression scores for incidents in which patrons were highly aggressive indicating that some aggression by staff in these instances had non-aggressive intent (e.g. to prevent injury); however, apparent intent had little effect on staff aggression scores in incidents with non-aggressive patrons. CONCLUSION: Although there is potential for staff to act as guardians or handlers, they often themselves became offenders when they responded to barroom problems. The practical implications are different for staff aggression with nonaggressive patrons versus with aggressive patrons.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".