Sustainable food systems and nutrition in the 21st century: a report from the 22nd annual Harvard Nutrition Obesity Symposium
Bibliographic record
Abstract
Food systems are at the center of a brewing storm consisting of a rapidly changing climate, rising hunger and malnutrition, and significant social inequities. At the same time, there are vast opportunities to ensure that food systems produce healthy and safe food in equitable ways that promote environmental sustainability, especially if the world can come together at the UN Food Systems Summit in late 2021 and make strong and binding commitments toward food system transformation. The NIH-funded Nutrition Obesity Research Center at Harvard and the Harvard Medical School Division of Nutrition held their 22nd annual Harvard Nutrition Obesity Symposium entitled "Global Food Systems and Sustainable Nutrition in the 21st Century" in June 2021. This article presents a synthesis of this symposium and highlights the importance of food systems to addressing the burden of malnutrition and noncommunicable diseases, climate change, and the related economic and social inequities. Transformation of food systems is possible, and the nutrition and health communities have a significant role to play in this transformative process.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".