Bibliographic record
Abstract
This paper is a study of the Canadian Medical Assistance in Dying (MAiD) program initiated in 2016 and undergoing expansion ever since. It tries to understand how it is that the Canadian government frames assisted dying as a viable and beneficial practice to both individuals and the public purse, while also exploring the rationale for its decision-making. The study begins by examining MAiD and its projected expansion (expected for March 2027) to cover a larger group of applicants than those initially qualifying when it was started. I argue that the program is first and foremost rooted in eugenics and economics as priorities in Canada at the government/administrative and society levels. Together with eugenics, I question economic forms of logic that shape how governments enact and support policies, specifically during periods of financial recession. In my overall analysis, I caution about the serious implications that legalized assistance in dying could have not only on the individuals directly affected and their immediate families and friends, but also on the larger society.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.024 | 0.033 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".