“I don’t want to say I’m broke”:
Bibliographic record
Abstract
Food insecurity, the inadequate or insecure access to food because of financial constraints, is an important public health concern, associated with poor physical and mental health. Recent research among post-secondary students shows that it also has consequences for academic performance; food insecure students are more likely to have lower grades and to drop out. This qualitative study aimed to describe the experiences of Queen’s University students who didn’t have enough money for food or who worried about having enough money for food. We conducted semi-structured interviews with 12 undergraduate, 10 graduate and 5 professional students. Participants included 14 students of colour and 2 Indigenous students. All described chronic food insecurity during their time at Queen’s, including 9 who experienced severe food insecurity, skipping meals and going hungry. Most participants cycled between different levels of food insecurity (severe, moderate, and marginal) depending on the availability of resources, though a few were severely or moderately food insecure on an ongoing basis. None escaped worry and anxiety about being able to properly feed themselves. Our sampling strategy netted a more diverse set of students than previously described in the literature on post-secondary student food insecurity, including first-generation Canadians, international students, Indigenous students, law students and undergraduate students transitioning to independent living. Our results demonstrate the human costs of market approaches to post-secondary education and lend support to the growing campaign in Canada for a basic income that includes young people.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".