Raised Food Prices Exacerbate Food Insecurity in Canada: A Call to Action
Bibliographic record
Abstract
Millions of Canadians are struggling to put food on their table due to record-high inflation rates.The skyrocketing food prices in Canada are an important contributor to food insecurity, which is defined as the inadequate or insecure access of food due to financial constraints.Food insecurity is a strong predictor of poor physical and mental health.People experiencing food insecurity who also have chronic health conditions are known to experience higher rates of mortality.Food insecurity is directly linked with poor disease management, and it is a predictor for increased disease severity for both communicable and non-communicable diseases.Food insecurity also correlates with higher rate of healthcare utilization and costs in Canada, with increased use of healthcare services and longer hospital stays.Addressing FI is a complex issue, which requires a comprehensive approach -a mix of short-term solutions that address immediate needs (food banks, community kitchens, community gardens) and long-term solutions (policy interventionprogressive taxation, minimum livable wage guarantees, public pension) that help to improve the economic status of lowincome households, given that the root cause of FI is inadequate income or poverty.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 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".