The Influence of Media Framing in a Participatory Democracy: The Genetic Non-Discrimination Act
Bibliographic record
Abstract
Genetic Non-Discrimination legislation arrived in Canada almost a decade after the US, UK, EU and Australia. Its birth was fraught: it took 5 legislative attempts, and finally succeeded against the opposition of a majority PM, Justice Minister, and Cabinet, not to mention a Supreme Court of Canada Challenge. This important legislation received little coverage in the press, and when it did, it was often at the urging of two opposing factions: civil society groups (Huntington's Society Canada, the Centre for Jewish and Israeli Affairs, Breast Cancer Canada) versus the Canadian Life and Health Insurance Association (CLHIA). Intense political lobbying was conducted by both parties, but a unique element of the civil society organizations was to promote a citizen writing campaign. In an analysis of media covering the Genetic Non-Discrimination Act (GNDA), or Bill S-201 (2017). I aim to investigate the effects of media framing in adequately informing the public about the GNDA. To do so, I conducted a literature review to identify key features of a participatory democracy and features impacting framing. Then, I developed a corpus of 88 articles, and identified 9 key pieces of media, including articles, YouTube videos, and opinion pieces. I analyzed how media framing influenced the dissemination of information regarding the GNDA. By shedding light on the media's pivotal role in creating an informed public within the realm of health policy, I underscore the importance and responsibility of media, and its potential impact on effective participatory democracy from flourishing.
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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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".