Corporate concentration and power matter for agency in food systems
Bibliographic record
Abstract
High levels of corporate concentration and power in agrifood supply chains raise important policy concerns because they can affect food systems in adverse ways. In this paper, we argue that increased corporate concentration and power in food systems has the capacity to undermine people’s agency– that is, their capability to make choices and exercise their voice. We explore three dimensions of the relationship between concentrated corporate power and people’s agency in food systems. First, dominant firms within highly concentrated food system segments can exercise market power, which enables them to earn excess profits – often by charging higher prices, suppressing wages, and weakening livelihood opportunities. Second, dominant agrifood firms have the capacity to shape material conditions within food systems – determining prevailing technologies used in food production, working conditions, levels of processing of packaged food items, and food environments – in ways that can affect people’s choices. Third, dominant agrifood firms can exercise political power by actively pursuing strategies to influence food policy and governance processes via lobbying and other more indirect measures, weakening opportunities for broader democratic participation in food systems governance. Given these potential outcomes, more policy attention should be paid to corporate concentration and its implications for agency within food systems.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".