Bike Theft Reduction at UBC
Bibliographic record
Abstract
The URSY 520 team undertook this engagement with the objective of delivering a suite of novel measures to reduce bike theft on the UBC campus, in an effort to remove this key barrier for students to bike to UBC. In order to do so, the project team first conducted a baseline analysis of both the status of biking within the mode-split of commuters to UBC, as well as of the status of bike security infrastructure on the UBC campus. This endeavor allowed the team to best understand the strengths and weaknesses of currently deployed solutions. Following the baseline analysis, a literature review and idea generation phase was undertook to develop a comprehensive list of 13 potential bike security and theft-prevention measures that may be possible. In order to provide a preliminary level of insight into the merits and drawbacks of each of the options on the aforementioned list, a multi-criterion filtering matrix was developed in consultation with the client, allowing the team to gain a better understanding of how each option performs on each of the key metrics. The initial filter highlighted above led to a qualitative assessment of the high-ranking alternatives, ultimately leading to a three-pronged solution that encompasses many of the options in the initial filtering matrix. 1. The Bike Kitchen Sharing Hub: refers to the sharing/borrowing of bike safety equipment, including U-locks, bike alarms, and GPS anti-theft devices. 2. Bike Storage Rooms & CCTV: refers to the introduction of bike storage rooms within buildings that are situated in high-theft areas, along with the use of CCTV for areas that this is not a possibility (subject to regulatory restrictions). 3. Theft-deterring Signage and Public Engagement: refers to the use of posters to deter thieves, along with the adoption of a comprehensive public engagement strategy, including social media campaigns, engagement programs, competitions, and a “BikeToUBC Week”. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".