Mapping Food Security Resources : Increasing Access and Building Connection
Bibliographic record
Abstract
Food insecurity among university students is a critical issue, with 76.9% of UBC Vancouver students reporting having experienced food insecurity or anxiety of varying degrees and 71.6% of those students unaware of using “financial aid [or] using institutional resources such as the Food Hub Market” (Rector et al., 2024). The purpose of this project is to address this gap (76.9% of food insecurity and 71.6% of unawareness) by improving the accuracy and depth of the content of the mapping of UBC’s food security initiatives and its targeted outreach strategies to raise awareness of these resources. The objective of this project is to understand barriers and limitations to food accessibility on campus, improve food asset resources, analyze needs, and promote quality interventions. Community-Based Action Research (CBAR) principles, which situates research in a defined community to study issues and implement interventions, combined with mixed methods, including both primary and secondary data collection with relevant data analysis, uncover barriers such as operational limitations, geographic disparities, and cultural mismatches while centering stakeholder voices (Gullion & Tilton, 2020). Findings inform co-designed project efficiency improvements, deliver recommendations on how to improve existing Food Asset Map, and advocate for informed institutional reforms to bridge gaps between existing services and student needs. Ultimately, seeking to increase awareness of resources, improve physical and cultural accessibility, and foster a campus culture where food support is accessible, equitable, and constructive. With the support of our client, UBC Health Manager and our SEEDS representative, this project covers data from 17 food security programs on campus in order to provide a comprehensive overview. This wide sampling allows us to capture a variety of challenges, from program operational difficulties to whether students can directly access and understand these measures. It is combined with quantitative surveys using tools such as Qualtrics. The use of these methods aims to confirm the prevalence of food security issues on campus, explore students' awareness and utilization levels of existing food security measures, and ensure that the questionnaire survey strictly complies with CBAR regulations. The total number of questionnaires is n=255, of which approximately 210 are valid responses. Among the valid responses, we found that less than 10% of the sample population indicated that they were completely unaware of food security measures, and 32% of respondents indicated that they were unsure whether they were eligible to use existing food security resources. This gap in eligibility awareness shows that even if students are aware of the existence of these measures, uncertainty about how to access them remains a barrier, which is also an important direction in which we believe that food security measures on campus need to change, that is, how to improve students' awareness of the eligibility of these measures, rather than just letting students have a general understanding of these programs. Although food insecurity may motivate students to seek out these resources, it ultimately comes at the expense of students. Therefore, the final project deliverables include recommendations on how to improve the interactive food security map and improve utilization strategies. These tools will ensure students have up-to-date access to all food security initiatives provided by the UBC Vancouver campus while fostering a more supportive campus environment. The collected data survey and further analysis could be used to improve food security initiatives and address the major barrier to access or utilization of food security resources for students. By doing so, our research aims to establish how to enable better or improve student access to food security initiatives, thereby providing practical insights to strengthen support for students to have access to enough food in sufficient quantities and consistent with their cultural values. 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".