Grounding global seeds: a contextual comparison of the politico-ecological implications of genetically modified crops for farming communities in Alberta (Canada) and Andhra Pradesh (India)
Bibliographic record
Abstract
The main objective of my dissertation is to analyze and compare the socio-ecological implications of the adoption of genetically modified (GM) seeds and alternative agroecological farming methods for farming communities in Alberta, Canada and Andhra Pradesh, India – localities situated in contrasting geopolitical, socio-cultural, and structural-institutional contexts in the global economy. For this research, the adoption of GM canola in Alberta and GM cotton in Andhra Pradesh are used as comparative case studies to explore the qualitative impact of agricultural biotechnology on farming communities. Many studies have examined the potential impact of GM crops, but few have looked beyond economic cost-benefit analysis. In this dissertation, I examine social and cultural aspects of farmer decision-making in the adoption of the new seed technology, farmer receptivity to new cropping methods, knowledge translation between laboratory and farmer, and the impact of global knowledge-based technology on local knowledge systems, socio-cultural practices, the nature-society relationship, and gender relations. I use a global ethnography methodology and draw on a series of field interviews with farmers to provide sociological insight into how global processes of the “Gene Revolution” impact different farming communities in different localities in the world-economy. In this dissertation I argue that the debate about the new agricultural technologies (e.g. GM seeds), the environment and agrarian crises should not be narrowed to the question of new technologies per se. Rather it should be understood from an agrarian political ecology perspective articulating political economy (neoliberal governance at global, national and provincial levels, and the processes of dispossession of primary agricultural producers from their means and conditions of production), socio-cultural systems (the construction of hegemonic discourse about genetically modified organisms, agricultural deskilling, gender relations), and ecosystems (a process of mastering nature, monoculturization, environmental risks, metabolic rift) in the context of neoliberal globalization. My fieldwork study of the “Gene Revolution” provides closer, more fine-grained research and analysis of its impacts with sensitivity to local class and status, gender and cultural issues, and the ways in which farmers’ technology adoption decisions can dramatically alter overall quality of life, local knowledge systems, community development, the sustainability of agriculture and the ecosystem itself.
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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 | 0.000 | 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 | 0.001 | 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".