Keeping Food on the Table; A Quarterly Review of the State of Hunger in Minnesota; 1st Quarter 2009; Child Hunger
Bibliographic record
Abstract
Hunger Solutions Minnesota released their second volume of Keeping Food on the Table, a quarterly review that tracks the state of hunger in Minnesota. This issue is a review of nutrition safety net programs that help alleviate child hunger. The first quarter of 2009 witnessed a record year-to-date increase in food shelf visits in Minnesota. Visits to Minnesota food shelves totaled 614,344 in the first quarter. That represents an overall increase of 28%. Child visits increased from 191,756 in the first quarter of 2008 to 236,652 in 2009. The recession, high food costs and 8.2% unemployment are factors that increase food insecurity in Minnesota. These factors continue to take a toll on low-income Minnesota families and their ability to provide nourishing meals to their children. Along with more need for nutrition support, Minnesota has seen a 6.2% increase in welfare caseloads in the past year. These factors increase the number of children eligible for safety-net programs. "The need for hunger relief in Minnesota is critical. The increase in child hunger is one of the economic benchmarks that drives HSM to increase access to safety-net nutrition programs like Food Support and the Summer Food Service Program," said Colleen Moriarty, executive director of Hunger Solutions Minnesota. "Food Support and other programs help to lift children and their families out of poverty." In the report, HSM also provides an overview and recommendations to Congress on the 2009 Child Nutrition Reauthorization Act. The Act will be renewed later this Fall. HSM believes that the programs within the bill can reduce childhood hunger and food insecurity. The safety net programs, such as school lunch, breakfast and the Summer Food Service Program, help improve child nutrition and health, and enhance child development and school readiness. Also in response to the rising need, Hunger Solutions Minnesota (HSM) launched the "Minnesota Food HelpLine" that provides a vital service to Minnesotans at risk for hunger. Callers to the Minnesota Food HelpLine (1-888-711-1151) will receive help with the Food Support (Food Stamps) program and referrals to emergency food assistance in their area. HSM provides multilingual Food Support application assistance and eligibility screening for callers statewide. The report includes statistics from the first month of the newly launched HelpLine.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".