Scientific clues on global food (in)security and climate change relationship as drivers of health
Bibliographic record
Abstract
Abstract Issue Food insecurity is in close relationship with the determinants of health. Global crisis including climate change (CC), natural disasters, poverty can deepen the burden, and all are linked with the economic, social, commercial, structural determinants of health. Defining such connections may help in proposing practical solutions. Description of the problem COVID-19 pandemic made the food security (FS) problem more visible. Food security considers basically the affordability, availability, and the quality of food. Food insecurity (FiS), violation of the right to healthy food, influences disease patterns and causes communicable and non-communicable diseases (NCDs). Globally, 71% of deaths are attributed to NCDs. Analyzing the relationship between FiS and other determinants of health like CC may be helpful for sustainable solutions in such a world where we are talking on “our planet, our health” motto. Example given in this study is the relationship between the country values/rankings of the “Global Food Security Index (GFSI)” and the “Climate Change Performance Index (CCPI)”. GFSI defines the FS situation and CCPI defines countries’ response to CC. Results Countries’ CCPI and GFSI values do not show a linear relationship. For example, Norway, as a country at the top of the Human Development Index (HDI) ranking list has both high CCPI and GFSI values. On the other hand, although USA and Canada have low CCPI, both have good GFSI values. Sub dimensions of the indicators may also vary across countries. Crisis like COVID-19, conflicts, poverty emphasize the need on improving the indicators in a transdisciplinary approach. Lessons Investigating indicators taking the determinants of health into account is helpful. However, different characteristics of the countries make it difficult to propose a standard approach to overcome the problems. Developing “new” indicators with transdisciplinary work might be useful in this sense. Key messages
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".