Bibliographic record
Abstract
With an increasing cost of living in Canada, more people are considered food insecure, and this can have a detrimental impact on Canadian University students (Dahal et al., 2023). Food insecurity is the state of being without reliable access to affordable and nutritious food. University students face greater food insecurity than the general population due to financial constraints and accessibility (Sing, 2022). This study sought to explore student’s knowledge, need, and use of food initiatives on campus (aimed at mitigating food insecurity) through the use of a questionnaire. The questionnaire determined that 61% of students are not always able to access affordable and nutritious food. Regarding food insecurity, students said that they never (39%), seldom (21%), sometimes (30%), often (9%), and always (1%) experience it. The University of Lethbridge has three food initiatives in place to combat food insecurity: the food bank, the food pantries, and the fresh food box. 27% of students in this study use the three mentioned food initiatives, breaking down into 9% using the food bank, 14% using the food pantry, and 4% using the fresh food box. This research could be used to help inform the University of Lethbridge’s levels of food insecurity in the student population as well as which food initiatives to implement based on students’ self-identified needs.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".