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Enregistrement W2956157517 · doi:10.1080/14649357.2019.1629197

Gigs, Side Hustles, Freelance: What Work Means in the Platform Economy City/ Blight or Remedy: Understanding Ridehailing’s Role in the Precarious “Gig Economy”/ Labour, Gender and Making Rent with Airbnb/ The Gentrification of ‘Sharing’: From Bandit Cab to Ride Share Tech/ The ‘Sharing Economy’? Precarious Labor in Neoliberal Cities/ Where Is Economic Development in the Platform City?/ Shared Economy: WeWork or We Work Together

2019· article· en· W2956157517 sur OpenAlex

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Notice bibliographique

RevuePlanning Theory & Practice · 2019
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueSharing Economy and Platforms
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésGig economyBATESWork (physics)Farm workersState (computer science)Precarious workEconomySociologyLabour economicsPolitical scienceEconomicsPrecarityLawAgricultureEngineeringHistoryArchaeology

Résumé

récupéré en direct d'OpenAlex

Over the past year, taxi drivers around the world have protested the appearance of ride-hailing apps Uber and Lyft, creating massive traffic slowdowns in London, Warsaw, Hong Kong, Paris, and Berlin. In the most extreme and tragic incidents, some taxi drivers have committed suicide publicly, naming economic desperation brought on by competition with app services as the reason. Proponents of the platform apps point to consumer convenience and lower (albeit heavily subsidized) costs, blaming the excessive regulation of the taxicab industry for its stagnation, but rarely speak to the cost for driver workers. Meanwhile, the ride-hail companies are fighting legal challenges around the world over using ‘freelance’ contracts to circumvent labour laws. As Uber and Lyft make their IPO debut on the stock exchange, drivers for the services in the U.S. have staged a strike, asking customers to boycott the apps in support of their protests about minimum pay and their status as independent contractors, not employees with full benefits and protections. Whether working for the disrupter or disrupted, the people providing the actual labour of driving customers are trying to address working conditions and wages in a new ‘platform economy’ city. In the previous issue’s Interface, Planning and the So-Called ‘Sharing’ Economy, authors from around the world considered the challenges of regulating platform apps based on their impacts on neighbourhoods, traffic, and the services they provide. In this issue, scholars and practitioners address how planning might consider these sharing platforms as they define work and the urban economy. The essays describe what the work of the sharing economy – both platform and informal – really is; consider the workers’ status as employees, and make a case for planners to engage more with the economic development aspects of growing platform app market. Zwick and Spicer’s and Kim’s essays are drawn from in-depth qualitative interviews with drivers for informal taxi services. These pieces explore the conditions of work, and what real alternatives are available for drivers, particularly for immigrants in the U.S. The ‘better than what I was doing before’ experience of drivers in Zwick and Spicer’s study suggests that economic restructuring and a loss of stable employment opportunities makes driving as a freelancer more attractive for some workers. Kim points out that as cities accept the ‘disruptive’ ride-hail apps’ presence despite their attempts to evade regulation, immigrant drivers in Los Angeles who were called ‘bandits’ are facing deteriorating wages with increased competition, while still being considered illegal. Kerzhner, considering home-sharing via Airbnb, asks the question; what work happens when the platform doesn’t acknowledge workers at all? While there are explicit debates about the drivers for ride-hail services, the hosts of Airbnb are a hidden labour force. The job of readying, renting, and resetting rooms and units often falls to women, whose efforts are under-compensated and may even disrupt formal employment. As Baber argues, these shifts towards flexible or ‘gig’ jobs are growing much more significantly with the rise of platforms during a moment when there are many workers looking to supplement unstable or low-paying jobs. The gig economy, she notes, benefits platform companies far more than it does workers or cities. Green takes this argument further to make the case for planners, specifically, to be far more active in the debate over platform apps as employers with responsibilities to labour, not just as go-betweens for customers and independent contractors. He views the question of jobs as integral to economic development, with planners taking a lead role envisioning equitable and sustainable urban economies. Finally, activist and practitioner Dominic T. Moulden describes the project of worker cooperatives – both in his organization in Washington, D.C. and their inspirations around the world. As the presence of ‘co-working’ spaces proliferates in his gentrifying city, he views the cooperative movement as an antidote to increasing competition for gigs – a way to plan a shared, sustainable economy. This two-issue Interface section has focused on the rise of the ‘sharing’ platforms that have become so prominent in how we live in cities – affecting transportation, housing, work, and community. These apps have grown quickly, and planning research and practice are now catching up with the wide-ranging impacts of their presence in cities. Authors have made important distinctions between ‘sharing’ and informal economic activities and the technology platforms used to make connections, and between the potential for wealth-building in locally rooted exchanges and the extraction of global technology companies. The cross-national examples presented point to the universality of the issues of planning for a platform city; they also provide opportunities for scholars and practitioners to ask how in their local economic and regulatory context, they might use examples from abroad.

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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)0,007
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,527
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0030,006
Science ouverte0,0020,001
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,058
Tête enseignante GPT0,262
Écart entre enseignants0,204 · 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