Communicating contentious issues in Canada: Analyzing media discourse of medical assistance in dying (MAID)
Bibliographic record
Abstract
Examining the ways medical assistance in dying (MAID) discourse is presented in the media, this thesis analyzes the key themes, issues and contentions found throughout the topic in Canada's two national papers, The Globe and Mail and the National Post.Through a mixed-methods approach of qualitative and quantitative means, this study examines the period in and around June 17, 2016 when MAID legislation came into effect up to July 1, 2019 using a thematic content analysis, framing analysis, and sentiment analysis approach.Collectively, these methods allowed for an in-depth analysis and breakdown of the ethical, moral, religious, and personal beliefs that contribute to key contentions around the topic of MAID, supplemented by five in-depth interviews among individuals with vested interest in the subject matter.Together, these methods aimed to explore the way contentious issues are presented in the media in the context of medical assistance in dying.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".