Response to Consutation Paper on Franchise Law
Bibliographic record
Abstract
he following is a response to the Manitoba Law Reform Commission's Franchise Law Consultation Paper published in May, 2007.The "Paper" addresses the question of whether Manitoba needs franchise legislation and provides a review of the state of franchise law in Canada, Australia, and the United States.Finally, the Paper poses a series of questions with regard to what should be done in Manitoba. 1 This response discusses the need for franchise law in Manitoba, drawing from the experience of other provinces.Having done so, a discussion of the issues raised in the Law Reform Commission's Paper will ensue. II. Is FRANcmsE LAW NEEDED IN MANITOBA?A. The Need for Franchise Law in ManitobaThe Legislative Assembly of Manitoba has already had the opportunity of discussing the question of whether franchise law is needed in the province.Jim Maloway, MLA for Elmwood, introduced Bill 18, The Franchises Act, during the yd session of the 35 th Legislature in 1992.The Bill followed the Alberta Franchises Act 2 very closely, prOViding for the delivery by a franchisor to a franchisee of a statement of material facts containing prescribed information, and further providing that no person shall trade in a franchise in the Province
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.044 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 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".