Debating Bill C-18: An Analysis of Power and Discourse in Parliamentary Proceedings on Canada’s Agricultural Growth Act
Bibliographic record
Abstract
" Bill C-18, Canada’s Agricultural Growth Act, amended several pieces of agricultural legislation and represents an important step in Canada’s efforts to modernize its agriculture and agri-food legislation. Although the bill received widespread support from many farm and seed organizations, the groups who critically opposed it cited potential implications such as increased corporate control, further restrictions to seed-saving practices, and financial hardships. How were these highly divergent perspectives accounted for within law and policy formation? Using a framework based on multiple forms of power, this article contributes to a broader and more integrated approach to exploring the ways power dynamics get articulated in law and policy debates. Discourse analysis of 32 parliamentary documents helps to shed light on a range of patterns regarding relations of power in the text and context of these debates. Based on this analysis, I discuss how varying and interconnected relations of power produced an imbalanced climate for agriculture and agri-food law and policy development—one that prioritizes economic freedom, global competitiveness, and private property rights. Further research regarding these varied and complex power relations is necessary for improving equity and accountability within these legislative contexts and, more generally, Canada’s agriculture and agri-food system. "
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.038 | 0.027 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".