Prescription in the Public Interest? Bill 207, The Medical Amendment Act
Bibliographic record
Abstract
I. !N1RODUCTION" W h e n there are [private members'] proposals that the government finds in the public interest, I think there is a more recent developing interest to work together and get these proposals moving."I Generally, the passage of Private Members' Bills ("PMB") Z into law is a rare feat for opposition members and government backbenchers ("private members").In the Manitoba Legislature, this statement is particularly true-since 1992, while 141 PMBs were formulated, 88 of which were printed and introduced in the House, only four subsequently became law. 3 It should, however, be noted that these figures do not account for PMBs which, after being introduced by private members but not passed, are introduced and subsequently passed in whole or in part through government legislation.Interview of Hon.Gord Mackintosh, Attorney General and Government House Leader, by Theresa Danyluk (6 October 2005) in Winnipeg,
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".