Bibliographic record
Abstract
The proliferation of smart phones with access to mobile data has revolutionized the for-hire transportation industry. New for-profit ridesharing firms offer taxi-style services by connecting paying passengers to a network of casually employed drivers via a mobile application. Unlike taxi companies, for-profit ridesharing firms can change their fares in real time to adjust to market conditions. The variable fare system encourages more drivers to work when the demand for rides is high, and it discourages drivers from working when they are not needed.1 This business model benefits consumers by providing an alternative transportation option that tends to cost less and arrive more quickly than comparable taxi services.2 Further, for-profit ridesharing's responsiveness to market conditions could help regulators achieve the elusive goal of matching supply and demand in the for-hire transportation market. Despite these benefits, legalizing and regulating for-profit ridesharing is ongoing struggle in many Canadian cities. For-profit ridesharing is a unique transportation service that demands unique regulatory treatment. A survey of the economic literature suggests that for-profit ridesharing suffers from only one major market failure: asymmetric information regarding safety. Passengers are not as well informed as their driver about the safety of their ride, and therefore might benefit from regulations that impose minimum safety standards. A regulatory regime that is focused on improving safety will improve market outcomes according to economic theory. Other common for-hire transportation regulations, such as fixed fares and entry restrictions, cannot be justifiably imposed on for-profit ridesharing and will worsen market outcomes if they are. The for-profit ridesharing regulations chosen by Canada's provincial and municipal governments are not always aligned with economic theory. An examination of four Canadian cities' regulations exemplifies a range of responses. Vancouver's ongoing ban on for-profit ridesharing is typical of many cities' reluctance to engage with the issue in timely manner. Vancouver and cities like it are missing out on the benefits that for-profit ridesharing offers. In Montreal, for-profit ridesharing services will be subject to the same regulations as taxis, including price and entry controls. This decision is misguided because price controls and entry restrictions on for-profit ridesharing do not rectify a market failure, and these policies come with an efficiency cost. The cities of Calgary and Ottawa have each adopted a regulatory regime that reflects economic rationale by focusing on safety and abstaining from entry restrictions and fixed fares. Other Canadian cities could benefit by following the model adopted by Calgary and Ottawa. That said, Calgary and Ottawa differ on how best to achieve safety for drivers and passengers. To make the most for-profit ridesharing, cities will need to be forthcoming with data that can help determine which safety regulations offer the greatest protection at the lowest cost.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".