THE WIC PROGRAM: BACKGROUND, TRENDS, AND ISSUES
Bibliographic record
Abstract
The mission of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is to safeguard the health of low-income women, infants, and children up to age 5 who are at nutritional risk. WIC provides nutritious foods to supplement diets, nutrition education, and referrals to health care and other social services. Administered by USDA's Food and Nutrition Service (FNS), the program has grown rapidly since its establishment in 1972, and is now one of the central components of the Nation's food and nutrition assistance system. Almost half of all infants and about one-quarter of all children 1-4 years of age in the United States now participate in the program. Federal program costs were almost $4 billion in fiscal 2000, making WIC the country's third-largest food assistance program in terms of total expenditures. WIC accounts for almost 12 percent of total Federal expenditures for food and nutrition assistance. This report presents comprehensive background information on the WIC program-how it works, its history, program trends, and the characteristics of the population it serves. It also examines issues related to program outcomes and administration. How the WIC community responds to these issues may have a large impact on future program operations.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".