Bibliographic record
Abstract
The University of British Columbia (UBC) has been monitoring transportation to and from their Vancouver Point Grey campus since 1997. The data collected has been used to inform planning decisions such as land use and transportation planning decisions. 1 In October 2014, UBC Campus and Community Planning published a new Transportation Plan for their Vancouver Campus to consolidate and update existing plans such as the 2005 Strategic Transportation Plan. The 2014 Transportation Plan identifies the need for, and commits to developing, a “comprehensive” on-campus transportation monitoring system. Until now, Campus and Community Planning has been reliant on surveys of the campus population to approximate on-campus mobility patterns. Under the guidance of Campus and Community Planning, through the Social Environmental Economic Development Studies Program, and with funding from the Alma Mater Society’s Sustainability Projects Fund, I conducted a pilot study using Global Positioning System (GPS) data loggers to monitor on-campus transportation patterns. By completing this pilot study, I am able to make recommendations to UBC Campus and Community Planning on the feasibility of using GPS technology for on-campus transportation monitoring. The week-long pilot study, consisting of 10 participants, has collected sufficient data to show the positive and negative aspects of the technology. While I have not conducted the pilot study in a statistically representative manner, some abstractions can still be made from the data that I have collected. This report concludes with a recommendation that GPS technology does indeed have a place in a comprehensive on-campus transportation monitoring system at UBC’s Vancouver campus. I make several cautions as to the fine scale accuracy of the technology and the ease of working with the data, but show that the end product still has the level of detail necessary to inform planning decisions. Finally, I note that the implementation of an exciting new technology such as GPS catches people’s attention, which in turn could lead to increased public engagement in sustainability and transportation planning, if the message is well presented. 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".