Bibliographic record
Abstract
BACKGROUND: A standardized global nutritional index (GNI) would provide a single statistic for each country according to its overall level of nutrition, which could then guide national policies. OBJECTIVES AND METHODS: We have developed a GNI modeled on the human development index (HDI), based on three indicators of nutritional status: deficits, excess, and food security. Calculations were made within four groups of countries (GNI) (32 developed countries, 26 countries in transition, 64 low-mortality developing countries, and 70 high-mortality developing countries) as well as between them-the Global Nutrition Index World wide (GNIg). RESULTS: Complete data were available for 192 countries. The ranking of the highest and lowest countries in the four groups (with their GNIg values) is as follows: developed countries--Japan 1 (0.989), United States 99 (0.806); countries in transition--Estonia 10 (0.943), Tajikistan 173 (0.629); low-mortality developing countries--Republic of Korea 12 (0.939), Nauru 185 (0.565); high-mortality developing countries--Algeria 47 (0.876), Sierra Leone 192 (0.420). A "double burden," in which nutrient deficits and excesses coexist in the same country, was seen in Mauritania (rank 139), South Africa (rank 146), Samoa (rank 157), Lesotho (rank 160), and Fiji (rank 169). The correlation between GNIg and HDI was intermediate (0.74, 55% of variance explained), demonstrating that good nutrition and development are not necessarily synonymous. Countries may be developed yet have a low GNIg (e.g., Australia, Canada, and the United States) and vice versa (e.g., Indonesia and China). CONCLUSIONS: Since nutrition is fundamental to a nation's health and productivity, the GNI and GNIg should be used alongside the HDI to obtain an optimal index of a country's overall well-being.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".