The Political Economy of Healthy and Sustainable Food Systems: An Introduction to a Special Issue
Bibliographic record
Abstract
Today’s food systems are contributing to multiple intersecting health and ecological crises. Many are now calling for transformative, or even radical, food systems change. Our starting assumption in this Special Issue is the broad claim that the transformative changes being called for in a global food system in crisis cannot – and ultimately will not – be achieved without intense scrutiny of and changes in the underlying political economies that drive today’s food systems. The aim is to draw from diverse disciplinary perspectives to critically evaluate the political economy of food systems, understand key challenges, and inform new thinking and action. We received 19 contributions covering a diversity of country contexts and perspectives, and revealing inter-connected challenges and opportunities for realising the transformation agenda. We find that a number of important changes in food governance and power relations have occurred in recent decades, with a displacement of power in four directions. First, upwards as globalization has given rise to more complex and globally integrated food systems governed increasingly by transnational food corporations (TFCs) and international financial actors. Second, downwards as urbanization and decentralization of authority in many countries gives cities and sub-national actors more prominence in food governance. Third, outwards with a greater role for corporate and civil society actors facilitated by an expansion of food industry power, and increasing preferences for market-orientated and multi-stakeholder forms of governance. Finally, power has also shifted inwards as markets have become increasingly concentrated through corporate strategies to gain market power within and across food supply chain segments. The transformation of food systems will ultimately require greater scrutiny of these challenges. Technical ‘problem-solving’ and overly-circumscribed policy approaches that depoliticise food systems challenges, are insufficient to generate the change we need, within the narrow time-frame we have. While there will be many paths to transformation, rights-based and commoning approaches hold great promise, based on principles of participation, accountability and non-discrimination, alongside coalition building and social mobilization, including social movements grounded in food sovereignty and agroecology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".