Redesign on the fly: Safer Bars and the Toronto experience
Bibliographic record
Abstract
In this paper we describe the experiences and lessons learned at the 18-month mark of the Safer Bars project, a three-year randomised control evaluation of an intervention to reduce bar violence. As part of the planning for the project, findings and lessons from bar-room intervention and observation research in Australia (Homel et al. 1997), the US (Saltz & Stanghetta 1997), the UK (MCM Research 1993), Canada (Graham & Wells 2001; Wells & Graham & West 1998), and elsewhere (see review by Graham 2000) were incorporated into the study methods and design. The project team also included investigators and consultants who were experienced with the workings of bars. In addition, the intervention had been tested extensively throughout the province of Ontario (see Chandler-Coutts et al. 2000). Nevertheless, as so happens in real-world research, the implementation of this large-scale project in Toronto, Canada encountered a number of challenges and setbacks. This paper describes the major challenges and how they were addressed.
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 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".