Bibliographic record
Abstract
Kramer attacks the Canada Prena-tal Nutrition Program (CPNP) be-cause it is based on the US Women, Infants, and Children (WIC) pro-gram. Yet on the basis of evidence that WIC reduced low-birth-weight births by 25 % and very-low-birth-weight births by 44%, the US Gen-eral Accounting Office concluded that WIC was a cost-effective pro-gram, resulting in savings of US$2.89 to US$3.50 for each federal dollar spent during the first 18 years of life.8 We should clarify that “providing milk, eggs and orange juice ” is only one component of the CPNP, which addresses a number of issues that af-fect women’s overall health, including drug and alcohol use, smoking, family violence and social isolation, in addi-tion to maternal nutrition and breast-feeding. We realize that Health Canada has a challenging task in evaluating the CPNP, and we look forward to the results. Although we agree with Kramer that more fund-ing should be allocated to research, this should not occur at the expense of other worthwhile interventions.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.880 | 0.741 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".