In November 2002, Saskatchewan Premier Lorne Calvert released The Premier’s Voluntary Sector Initiative: A Framework for Partnership between the Government of Saskatchewan and Saskatchewan’s Voluntary Sector.1 This is the province’s own
Bibliographic record
Abstract
version of the federal government’s Voluntary Sector Initiative (VSI) announced in June 2000.2 Many other provinces, like Saskatchewan, are currently undertaking work related to their government’s relationship with their voluntary sector. In Saskatchewan, one of the intended purposes is to increase the awareness of the value of voluntary sector activities to the overall well-being of all citizens. The stated intentions of the Premier’s VSI are commendable in wanting to develop an open, collaborative, sector-to-sector relationship between the public sector and the so-called voluntary sector. The document deserves kudos as well for affirming (in the “background ” section) the importance of “understanding Sas-katchewan’s sectors and their relationships ” (p. 3). It stresses that the two sectors interact continually, need to enter into a dialogue, and that government could not do without the accomplishments of voluntary sector, particularly in the fields of health and social service delivery. This is recognition that service delivery in these domains is not fully addressed within the public sector alone. Among other positive and noteworthy contributions of the document is its
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".