Bibliographic record
Abstract
This commentary supports an ‘opt-out’ system for organ donation in Canada. To begin, it examines the state of organ donation in our country and presents both the ‘opt-in’ and ‘opt-out’ schemes. Then, it argues in favour of implementing ‘opt-out’ legislation in Canada, suggesting that this system makes donation easier for families and improves the donor rate. Opinions against ‘opt-out’ are considered and debated. Finally, other donation systems as well as potential methods to encourage organ donation are briefly discussed. RÉSUMÉ Ce commentaire appuie la mise en place d’un système avec option de retrait (‘opt-out’ system en anglais) pour le don d’organes au Canada. Tout d’abord, ce commentaire se penche sur l’état actuel du don d’organes dans notre pays et présente à la fois le modèle à option d’adhésion (‘opt in’) et celui à option de retrait (‘opt out’). Puis, il argumente en faveur de la mise en œuvre d’une législation qui permettrait un système avec option de retrait au Canada, suggérant que cela faciliterait le don d’organes pour les familles et amé- liorerait les taux de dons. Certains des arguments contre le système avec option de retrait sont examinés et démontés. Finalement, d’autres systèmes de dons, ainsi que des méthodes pouvant possiblement encourager le don d’organes, sont discutés brièvement.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".