Bibliographic record
Abstract
This article compares and contrasts automobile insurance provisions across Canadian jurisdictions, with particular emphasis on comparing how British Columbia (BC) fares relative to other provinces. A brief discussion of the different automobile insurance regimes is provided, as well as the mandated packages in each province. Price quotes are obtained by jurisdiction for the mandated package as well as for enhanced packages, for a hypothetical driver (either male of female) with a driving record that is good or poor, who is 45 years of age and drives a Honda Civic. We find that prices in Vancouver, BC are in the middle of the pack, and are much lower than in Toronto, Ontario for a driver with a good record. BC average prices are similar to those in Saskatchewan and Manitoba. Loss ratios vary quite a bit over the period 2011-2015, with no clear pattern except that they are always higher in public regimes than private ones. Because these ratios fluctuate from year to year, no one single province has performed consistently or significantly better than the others. One must be very careful when drawing hard and fast conclusions because of the differences in insurance packages across provinces and the aggregated and limited nature of much of the available data. Four conclusions are notable: (i) Automobile prices charged in BC are in line with those of Manitoba, (ii) a driver in Vancouver pays significantly less than an otherwise comparable driver in Toronto, (iii) in the private system, Ontario has the lowest loss ratios while, in the public system, there is no discernable, stable, relationship across the jurisdictions, and (iv) average claim costs cannot be compared across regimes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".