Exploring the language of death with dignity: a comparative and critical content analysis of Canadian news editorials
Bibliographic record
Abstract
My major research paper (MRP) focuses on the language and arguments used in the debate surrounding medically assisted dying. This paper was interested specifically in how arguments are framed, and if arguments have changed regarding medically assisted dying in the past twenty years. My central research questions are: what are the arguments on both sides of the debate used in news editorials? And if the arguments changed – how did they change? To answer these questions I compared two case studies: (1) Sue Rodriguez and (2) Gloria Taylor. To compare the two cases I analyzed the editorial pages and online comments of major Canadian newspapers. I used key words in context (KWIC) to identify frames and arguments used. Six frames emerged: medically assisted dying legal (ML), medically assisted dying medical (MM), medically assisted dying moral (MMM), pro-life legal (PLL), pro-life medical (PLM), and pro-life moral (PLM). The frames in support of medically assisted dying were used more than double the amount that pro-life frames were used; they were also used more frequently in 2012 than they had been in 1994. Further, there fewer overall KWICs used in 2012, but they were used correctly more often than in 1994. These findings suggest that the act of medically assisted dying is better understood and defined, and that it seems to have more support now than it did twenty years ago.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.002 | 0.005 |
| 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".