Bibliographic record
Abstract
Like the rest of Canada, the vast majority of Quebecers do not have access to comprehensive, quality, palliative end-of-life care. Nevertheless, despite every substantive argument, compelling study, troubling precedent and cautionary tale regarding physician-hastened death, the Quebec Government has passed Bill 52 - a Bill legalizing euthanasia or what is euphemistically being called medical aid in dying (MAD). While the Bill purports to ensure that "everyone may have access, throughout the continuum of care, to quality care that is appropriate to their needs, including prevention and relief of suffering," it states that organizational structures, institutions and palliative care hospices will carry out this mandate "within the limits of the human, material and financial resources at their disposal." Perhaps, given the limitation of those resources, the only detail Bill 52 provides regarding how they will fulfill their mandate pertains to the administration and tracking of MAD. How will Quebecers feel when they realize that while their healthcare system can offer them euthanasia, it cannot assure them or their loved ones, access to healthcare professionals proficient in palliative care? All of which begs the question, is it really time to get MAD?
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".