Bibliographic record
Abstract
The Canadian healthcare system is costly. Each day, health leaders must make decisions about what healthcare services will be offered, how they will be funded, to whom they will be made available, and within what administrative and clinical structure they will be managed and delivered. These decisions, their justification, and the ethics framework employed can vary greatly across the Canadian landscape. These high-stakes decisions must not only draw upon healthcare science but the science of business finance, risk management, and organizational design. However, in equal measure and often overlooked, these decisions must draw upon our values, upon our ethics. Sometimes we get it right, and other times, decidedly less so. When timely and fair access to effective and efficient healthcare services is not rendered, matters of justice, fairness, rights, and a host of other constructs are often cited. However, these important constructs are commonly misunderstood, contributing, in part, to well-intentioned but ultimately unethical decisions.
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 | 0.070 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.098 |
| Scholarly communication | 0.028 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".