Bibliographic record
Abstract
The Middle East and North Africa (MENA) region covers countries of Western Asia and North Africa. An ethnically and socioeconomically diversified region, MENA encompasses more than 360 million Arab, Persian, Jewish, and Kurdish inhabitants dispersed across nations that range from the oil-rich to the resource-scarce [1, 2]. Opinions vary as to what and how many countries make up this region [2–4]. For the purpose of the current chapter, a commonly recognized map of the region (refer to Fig. 5.1) is adopted. The region has witnessed great progress on multiple socio-health indicators over the last decade; this is shown by an average life expectancy of 71 years, an under-5 mortality rate of 38/1,000, and a decline in the prevalence of underweight and stunting in children under 5–12 % and 25 %, respectively [2, 5]. Despite this encouraging trend, malnutrition, whether in deficiency (undernutrition) or excess (overweight/obesity), remains a chief contributor to the national and global burden of disease. Both these conditions can coexist in the same county forcing it to deal with the high cost of treating diet-related diseases while trying to set up a national plan to combat nutritional deficiencies [3, 6].
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".