Measuring Resilience and Creating a Culture of Preparedness
Bibliographic record
Abstract
● The scope of the project is to create a culture of resilience as well as arrive at the performance matrix to measure it. This culture shall help UBC as well as City of Vancouver to reduce the impact from acute shocks and be more resilient ● The methodology involves identifying current state of resilience via survey (online as well as offline, in different language). The survey shall be conducted by people amongst the community of people, who are well knit via social circles, such as, clubs ● Depending on the results of the survey, next steps are decided and another survey is conducted to measure implementation of the same. Thus, it is an iterative process, with small progress at each step, ultimately reaching the ideal state of urban resilience ● The recommendations are centred around two broad themes –organization to community and community to community. The latter forms a part of social cohesion and for better implementation, both of the themes need to be implemented together ● Parallels can be drawn between City of Vancouver as well as UBC, however, the scope has been presently focussed on using UBC as a pilot, and eventually rolling to the city. 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 | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".