Freedom v. Protection (v. Fence-sitting) narratives in the euthanasia debate: a qualitative narrative policy analysis of Canadian media from 2007-2017
Bibliographic record
Abstract
In 2016, Gray and Jones adapted the narrative policy framework (NPF) to a qualitative context. In this research, I build from their resulting Qualitative NPF (Q-NPF) method to analyze 300 randomly selected Canadian media articles published between 2007-2017 on the topic of Medical Assistance In Dying (MAID). I begin by explaining how the concrete procedures of MAID are distinct from other end-of-life practices, and introduce the terminology that will be used throughout this research. I then introduce historic and academic literature relevant to the form and content of the contemporary media narratives to be analyzed, especially drawing theoretically from Rose’s (2013) discussion of biomedical personhood discourses and Butler’s theory of unevenly distributed precarity. I then explain the methodology of qualitative narrative policy analysis (Q-NPF), and apply it to Canada’s MAID debate by dividing the policy positions into what I call the Freedom, Protection, and Fence-sitting narrative policy camps. The Freedom camp advocated for MAID legalization; the Protection camp advocated against MAID legalization; and the Fence-sitting camp avoided advocating either for or against baseline legalization of MAID, instead weighing in only on peripheral issues. I discuss the qualitative differences of narrative content specific to these three camps, highlighting the most prominent narrative trends (by frequency of publication) and discussing the ways in which these findings either accord with or contradict the expectations of the literature review. Finally, I update the reader on Canadian legislative developments since 2017 and identify how the data of 2007-2017 anticipated these developments, demonstrating the salience and predictive power of Q-NPF. I conclude by proposing new directions for potential investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".