Seeding Sustainability Over Extracting Capital: Advancing a Vision for Technology Justice in the Canadian Agri-Food Sector
Bibliographic record
Abstract
The detrimental consequences associated with industrial models of food production are becoming more difficult to ignore. In response, one dominant approach to mitigating the myriad environmental, social, and ethical harms relating to food has sought to increase the efficiency of agricultural outputs through scientific and technological innovation. Although technology certainly has some role to play in any vision of a sustainable future, technocratic approaches to problem solving are insufficient—and arguably inappropriate—for addressing many of the kinds of complex challenges that we face today. There are recent indications that both agri-food law and policy and innovation policy are being taken more seriously in Canada, which creates an opportunity to reflect more deliberately on their ends and means. This dissertation explores the topic of how laws, policies, and other tools of governance can work to better align technological innovations in the agri-food sector with shared environmental goals and ethical aspirations. Taking a critical legal perspective closely informed by feminist insights and the work of existing, analogous justice movements, I examine several interlinkages between technology, law, the environment, and society to evaluate some of the failings of existing approaches to food systems transformation and to offer a contribution to the conversation about alternative pathways. Given the context-specific nature of food systems and food systems governance, my focus is primarily on Canada, but the universal importance of food in a globalized world renders some comparative and transnational discussion unavoidable. I use case studies and discourse analysis to demonstrate that, when considered through a justice-oriented lens, several of the new and emerging technologies being championed in the agri-food sector may not be as beneficial as their proponents claim. Instead, they may serve to retrench injustice and cement existing, exploitative power structures, making them more difficult to challenge and change later down the line. Thus, if technologies are to serve public instead of private interests in the ways they are incentivized, designed, regulated, and used, we will need to see broad systemic and structural reforms informed by thoughtful shifts in our values and priorities, rather than merely reactive adjustments to our policies and practices. Though this undertaking will be difficult, it is not impossible; this dissertation offers one way to facilitate the process of seeding change for environmental sustainability and technological justice.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.052 | 0.080 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".