Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,006 | 0,010 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,008 | 0,002 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».