A Rapid Review of Territorialized Food Systems and Their Impacts on Human Health, Food Security, and the Environment
Bibliographic record
Abstract
The global food system is facing multiple problems, including rising food insecurity, degrading environments, and an increased incidence of diet-related chronic diseases. International organizations are thus calling for a transition toward territorialized food systems to alleviate some of these challenges. Yet, limited evidence supporting the benefits of territorialized food systems is available. Our objective was to summarize the current body of literature on territorialized food systems and their impacts on human health, food security, and the environment using a rapid review methodology. Articles were retrieved from three databases and analyzed using keywords and inclusion criteria corresponding to territorialized food systems, environment, human health, and food security. Six relevant publications were identified. While this limited evidence suggests that territorialized food systems may have positive effects on all three dimensions, data are not consistent across publications. For example, territorialized food systems may contribute to improved diet quality, provide agroecosystem services, and contribute to food security. However, food produced within these food systems may have a higher carbon footprint and be less available than industrially produced food. This rapid review also highlights the siloed nature of the current research on territorialized food systems and emphasizes the need for more holistic and interdisciplinary research.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".