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Maintaining Local Values in the Face of Digital Platforms

2021· article· en· W7029990721 sur OpenAlexaboutno aff

Notice bibliographique

RevueeYLS (Yale Law School) · 2021
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueSharing Economy and Platforms
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTechnocracyLiabilityRentingBusiness modelSharing economyTourismGeneral partnershipValue propositionAccommodation
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Airbnb. Lime. Uber. Sidewalk Toronto. These and other digital platforms and technologies increasingly shape city life, and some academics fear that tech companies’ values will clash with those of public authorities. Other researchers emphasize the opportunity for public authorities to collaborate with tech companies to prioritize shared values through coregulation. Recently, Sofia Ranchordás and Catalina Goanta at the University of Groningen and Maastricht University, respectively, have proposed that cities and tech companies form partnerships in which they act as “co-creators of business opportunities which benefit local communities.” Ranchordás and Goanta acknowledge that digital platforms have created value in new forms of communication and lowered barriers to market entry for smaller businesses. But Ranchordás and Goanta caution that digital platform companies hold values that may at times conflict with the interests of public actors and local communities. They recommend that local governments “be aware of the risks of technocratic discourses and potential conflicts between platform and local values.” The influence of digital platforms on society has grown as these new technologies developed into effective regulators of behavior and providers of communications and dispute resolution. For instance, rental platform Airbnb connects travelers seeking accommodation with hosts in its online marketplace. To use the platform, both hosts and travelers must agree to Airbnb’s terms of service—the legally enforceable rules governing business on the Airbnb platform. Through the terms of service, Airbnb creates norms for users with a code of conduct, such as a non-discrimination policy. Airbnb also provides exclusion of liability clauses, which eliminate responsibility for itself and its hosts in ways that sometimes conflict with local laws. As digital platform companies grow and change the economy, governments have modified their legal frameworks to accommodate and regulate them. Cities such as Amsterdam initially provided an informal arrangement to Airbnb to operate despite local laws restricting tourist accommodations in homes. Ranchordás and Goanta contrast Airbnb’s goal, to generate profits through rentals, with public interest values such as preventing overcrowding. They assert that the rising influence of digital platforms can lead to the dominance of the companies’ values over public values, with problematic effects such as the “decharacterization of neighborhoods, exclusion of residents from the city center, and gentrification of traditional urban centers.” Ranchordás and Goanta argue that digital platforms affect public values that cities prioritize such as neutral service provision, livability, and economic growth. A platform such as Airbnb may provide tourist revenues, convenience, and flexible employment to a city. These benefits may be attractive to some cities, but the accompanying effects of rising housing costs and transient visitors may be unwelcome for other localities that prioritize affordability and family-friendliness. The values of digital platforms may conflict with a given city’s values, a potential tension which Ranchordás and Goanta contend has grown more challenging as digital platforms play a greater role in shaping public services. They argue that, despite differences in some values, digital platforms and public authorities may cooperate in pursuing others. For example, digital platforms might support the efficient administration of municipal rules. Cities often lack the resources to monitor and enforce all of their rules. Although a city’s regulation on how many days a home may be rented would entail a volume of inspections beyond most cities’ capacity, Ranchordás and Goanta suggest that cities could partner with tech companies to apply the regulations at the platform level with greater fairness and consistency. Outsourcing infrastructural needs to digital platforms could also enable cities to serve communities through data-driven solutions that cities are unable to provide themselves, Ranchordás and Goanta claim. They assert that transportation modes such as micromobility, short-distance transportation via light vehicles like bicycles and scooters, that operate via digital platforms can also serve city sustainability goals. They highlight Romanian bikeshare company Pegas’s experience in Bucharest, a city which had struggled with earlier attempts to promote sustainable mobility. Pegas provided several thousand bicycles that users shared via an app, in turn supporting the city’s efforts to combat pollution and traffic. Moreover, Ranchordás and Goanta argue that other synergistic opportunities may arise if digital platforms take public authorities as clients, in addition to the individuals and businesses that are digital platforms’ traditional customers. Such public-private partnerships could facilitate discussions of each side’s values to help cities safeguard public values and still provide the benefits created by digital platforms’ data and technological expertise, they claim. Ranchordás and Goanta envision a municipal legal framework that allows tech companies to develop “products and services that have a direct impact on public infrastructure” and that focuses on the “legal duty to negotiate the conditions of the economic activity with the municipality in good faith.” They predict that such a framework could reduce the legal uncertainties that some tech companies historically have taken advantage of to disrupt markets. As digital platforms continue to emerge and influence aspects of public life from transportation to housing, Ranchordás and Goanta argue that cooperation can help local authorities ensure their communities receive valuable services without encroaching on local values.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,246
Score d'incertitude au seuil0,517

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,020
Tête enseignante GPT0,228
Écart entre enseignants0,207 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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