Bibliographic record
Abstract
"Funding Long-Term Care in Canada: Issues And Options" (Adams and Vanin 2016) is a well-argued paper that grounds its recommendations in learnings from other jurisdictions that have tried to enact major reforms to the funding approach for long-term care (LTC). In particular, the paper considers the experience in both the UK and Quebec. The paper correctly highlights the significant difficulty of implementing large structural reforms to deal with LTC funding challenges. This is not a surprising result. Structural reform in healthcare has proven challenging even when the problems being addressed are immediate and large in scale, let alone for those that will manifest themselves in increments over many years or even decades in the future. The authors are correct, in my view, to focus on the art of the possible and to seek ways to set Canada on a path towards addressing the coming challenges around LTC, rather than proposing a big-bang fix.
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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.030 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.069 | 0.085 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".