Homeless “Squeegee Kids”: Food Insecurity and Daily Survival. A study of food habits among homeless youth in Toronto
Bibliographic record
Abstract
Food insecurity and homelessnessFood insecurity has been defined as the "limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire acceptable food in socially acceptable ways" (Anderson, 1990).In the 1990s, food insecurity among low-income households in affluent Western countries became an increasing concern, and considerable research was conducted on the causes and effects of food insecurity among low-income households and families.Little research has been done, however, on the relationship between homelessness and food insecurity.In an attempt to fill this gap, the authors undertook a study of homeless youth who frequented a downtown Toronto drop-in.What does food mean to teenagers and young adults in the chaotic world of life on the street?Where, how and what do they eat?How does their precarious access to food affect their health?The answers to these questions have implications for agencies and organizations that work with street youth. The study approachResearch was conducted at a downtown Toronto drop-in centre that is open on weekday afternoons, and is visited by 80 to 100 people a day.Although the dropin was used mostly by adults, at any time there were usually 5 to 15 youth present, clustered around one or two tables.The drop-in provided cooking facilities, but no food, although coffee and tea were available.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".