Unpacking Frame Resonance: Professional and Experiential Expertise in Intellectual Property Rights Contention*
Bibliographic record
Abstract
In 2004, Canadian officials introduced amendments to the country's Plant Breeders' Rights Act. An intellectual property movement supported the changes and a farmers' rights movement opposed them. Though conditions seemed to favor the former, the latter was more successful. To explain this, I compare each movement's deployment of professional and experiential expertise in their framing attempts. I argue that professional expertise, acquired through formalized training, and experiential expertise, gained through lived experience, provide unique and important support in claims making; highly resonant frames are often those built and maintained with both. Indeed, the farmers' rights movement's use of professional and experiential expertise together in framing the amendments helps account for its efficacy against the intellectual property movement (which failed to do so). This analysis contributes to our understanding of frame resonance and highlights underexplored South-to-North channels of influence due to the particular role of Southerners' experiential expertise in this comparison.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".