Bibliographic record
Abstract
Arguments about where private sector healthcare delivery fits within a publicly funded system should distinguish among types of private delivery. In Canada, most healthcare delivery is already private, albeit not-for-profit (e.g., hospitals) or small business (e.g., physicians, dentists). The expectation that corporations provide a return on investment to shareholders is more problematic if the dual loyalties that professionals have as agents of their patients conflict with the profit imperative. Consideration of where such firms can generate their profits, and the "production characteristics" of healthcare, suggests that certain sectors lack the contestability, measurability and complexity needed to make competitive markets function effectively. Neither is it likely that competition can co-exist with requirements for a single payer. In that connection, it must be recognized that the incentives inherent in a corporate structure, all other things being equal, appear inimical to many desired outcomes of a healthcare system. These tendencies can be controlled, but only through fairly elaborate measurement and monitoring of performance, which carry their own costs, and which smaller providers may be unable to meet. Chodos, MacLeod, Romanow and Kirby have done a great service of reminding us where we want to go--and where we do not.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.029 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 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".