Improving well-being through food sovereignty : a meta-narrative literature review
Bibliographic record
Abstract
Industrialized agriculture and food security interventions have failed to eliminate global hunger, while creating complex environmental, health, and well-being challenges. The food sovereignty movement, which recognizes the power imbalances and social inequities in the global food system, presents a new lens through which to design interventions to improve how agricultural practices impact individual and community well-being. This thesis project answered the following research question: how can food sovereignty frameworks incorporate assessments of health and well-being? This research contributes to the gap in our understanding of the importance of food sovereignty practices to health and well-being through a meta-narrative literature review. Four well-being narratives (environmental, physical, cultural-spiritual, and social-political-economic) were identified from the literature and used to develop a novel framework demonstrating the relationship between food sovereignty practices and multi-dimensional well-being outcomes. A set of n=37 indicators were developed and organized into four themes of environmental, physical, cultural-spiritual, and social-political-economic wellbeing, to assess the relationship between food sovereignty practices and multiple forms of well-being. This study demonstrates how the application of food sovereignty practices can influence the well-being of individuals, their environments, and communities. As well, the results of this work emphasize the importance of defining well-being holistically, rather than viewing well-being outcomes from a purely biomedical health perspective. This framework presents a way for future researchers, farmers, and agricultural organizations to begin measuring well-being outcomes that result from their food production practices.
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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".