Bibliographic record
Abstract
Connoralthough the book is about health policy, it is in part historical in structure; a portion of it attempts to capture the development of Newfoundland and Labrador's health-care infrastructure since the 1930s.Third, I believe that any review of A Health System Profile should take a much broader intellectual perspective than the narrow facts and figures it presents -how has health care of Newfoundland and Labrador, along with the health of its inhabitants, changed (or not) over time?What are the big trends over the long run, not just, for example, those of the last decade?Relatedly, it strikes me there is a good argument for a comparison between this 2021 publication and previous historical sources.Finally, and perhaps most important, one of the book's significant conclusions addresses the "Newfoundland paradox."This paradox arises from the tension between the fact that every standardized and objective biomedical/scientific metric used to gauge the health status of any population demonstrates that Newfoundland and Labrador is the unhealthiest province in Canada, and that individuals living there steadfastly believe they are the healthiest of people.The paradox that this jurisdiction can be labelled the "sick man" of Canada, but yet is also one rejected by its people, I will argue, has curious and interesting historical parallels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.055 | 0.023 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".