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Record W7029990721

Maintaining Local Values in the Face of Digital Platforms

2021· article· en· W7029990721 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsTechnocracyLiabilityRentingBusiness modelSharing economyTourismGeneral partnershipValue propositionAccommodation
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.228
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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