Cows in the city: Canadian dairy farmer protests in Ottawa
Bibliographic record
Abstract
It is not very often that you see a cow or tractor in downtown Ottawa, in front of the home of the Canadian Federal Government. However, this has happened in both 2015 and 2016 in response to international trade agreements. This study explores different factors impacting dairy farmers decisions to protest international trade agreements in Ottawa. The theoretical foundation for this research is political opportunity theory, which is further expanded by the complementary theories of multi-institutional politics perspective and resource mobilization theory. The theoretical approach is deepened through exploring the rich history of Canadian dairy and how that history informs dairy farmers material and non-material interests when understanding international trade agreements. The study uses qualitative methods of semi-structured interviews and life history interviews over two months of data collection, which in turn centred the voices and perspectives of dairy farmers. Moreover, these methods were enriched through the use of visual ethnography to elicit further emotions or perspectives from farmers through images of historic and current dairy protests. The study found that dairy farmers have concerns about the dairy industry, much of which tended to focus on their families, farms, and futures. Through the interviews, it was revealed that dairy farmers were strategic in the timing and choice to protest in Ottawa beyond location with sophisticated mobilization efforts. However, there is also significant tension the motivations for protesting and how dairy farmers interpreted the protests varied across all participants. Overall, the study concludes that protests in the future will need to adapt and change to reflect dairy farmers’ vulnerabilities that could be further exacerbated by tensions within policy priorities, protest choices, and popular opinion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".