Bibliographic record
Abstract
from the appointment of two Senior Fellows, one from the University of Regina and the other from the Government of Saskatchewan. The fellowships have each been for twelve months, and are designed to recognise scholars and practitioners who have made significant contributions in the area of public policy. During their year at SIPP, the Fellows have pursued their public policy research interests and participated in the various activities of the Institute. At the conclusion of their appointment, SIPP publishes the research the Scholars have undertaken during their term at SIPP. The research results are published in our Scholar Series, thereby disseminating to a wider audience the ideas and findings of the Fellows. Mr. Dan Hickey, the 2005-06 Government of Saskatchewan Senior Fellow, examines the facts of provincial health spending and financing in an effort to better understand current and future policy challenges for Saskatchewan. Mr. Hickey’s time at SIPP was cut short as he assumed a new position in health care administration in the Province of New Brunswick in 2005. Despite leaving the one-year appointment early to take advantage of this new opportunity, Mr. Hickey submitted his research findings and we are pleased to publish it and make it available to the policy community.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".