Can public universities play a role in fostering seed sovereignty?
Bibliographic record
Abstract
Across Canada and the United States, public universities were founded with a mission to contribute to broad societal well-being. Yet, the capacity of public research institutions to develop and disseminate flexible and accessible tools for resilient agriculture has been challenged in recent decades. The role of universities in advancing extractive, rather than regenerative, economies has been amplified by the privatization of public agricultural research and extension of knowledge to farmers, particularly in plant breeding and plant genetics. In this article, we examine the history of public research for seed systems in North America through a “seed regimes” framework, arguing that a narrow focus on commercialization of public research has exacerbated inequalities inherent in the founding structure of public agricultural research, including the displacement of Indigenous land and seed relations. We then discuss how community organizations are challenging the enclosure of seed through seed sovereignty organizing and freelance plant breeding, in some cases through the development of community–university partnerships based on the principles of the cocreation of knowledge. We conclude by offering a reimagined public seed research agenda that focuses on strengthening links between public research and grassroots seed movements, as an opportunity to build more resilient seed and 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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".