The Good Samaritan Protection Act: You Can Lead a Horse to Water, but You Can’t Make It Drink
Bibliographic record
Abstract
hen faced with an emergency, many people fear making a bad situation demonstrably worse.The idea that "if I do nothing, it's not my fault if it gets worse" is imbedded in the minds of many Manitobans-and rightly so.There is no positive duty for persons to act when faced with a crisis.What Manitobans are afraid of is the common-law principle that governs assistance in situations where a person has already suffered an injury.Though all law students learn this common law principle in their first year at law school, there may be people unfamiliar with the concept, and it bears repeating here.Individuals who provide assistance in emergencies can be held liable if their attempt to provide relief exacerbates existing injuries or inflicts new injuries.1 While would-be rescuers may not turn their mind to the fact at the time, if they make the situation worse, they could be sued for negligence and damages could be awarded to the extent that they made an existing medical emergency worse.Enter The Good Samaritan Protection Act. 2 Legislators specifically designed this bill to provide partial immunity from liability to those providing emergency assistance, except in cases of gross negligence.Both the Liberal Party of Manitoba (Liberal(s)) and the New Democratic Party of Manitoba (NDP) went to great pains to pass Good Samaritan legislation.After some negotiation, legislators resolved that there would be bipartisan movement on the bill sponsored by the NDP member, and Bill 214 ultimately became law on 7 December 2006.3 There are, however, serious questions as to whether such legislation was truly necessary.This paper will provide a history of Manitoba's 1
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".