Data for the Night: Digital Rights, Trust, and Responsible Engagement with Data in 24-Hour Cities
Notice bibliographique
Résumé
The emergence of a field called “Night Studies” over the last 15 years brought to light a much-needed approach to how urban governance is applied to the 24-hour cycle. The urban night is a complex ecosystem encompassing policies and data related to mobility, healthcare, media, culture, entertainment, service industries, and much more. The closing down of the nighttime economy was one of the first and most dramatic social effects of the COVID-19 pandemic’s arrival in the West, following a decade in which the night of cities was a significant focus of interest from several quarters. These included city administrators and urban planners, data activists, institutions in the cultural field, and scholars engaged in measuring, understanding, and evaluating the night. Even if the increasing datafication of urban spaces has impacted the city after dark, this aspect of urban life is rarely addressed by the overlapping debate of data and policy beyond public lighting and surveillance in smart cities. While certain cities rely heavily on big data to understand and manage their territories in real-time, the lack of open data to comprehend the night is still an issue. For years, discussions of the night have happened in isolation from discussions of data policy and various forms of urban intelligence. The lack of data focusing specifically on the nighttime economy, and broader analyses of the impact of a smart city agenda beyond daylight, leave stakeholders – municipal governments, small business owners, neighbourhood associations, night-shifters, advocates, and communities – navigating challenging circumstances without essential information. It is worth highlighting that the night is also a space for various marginalized communities whose exposure through open datasets and thoughtless policies might cause harm to its members, notably unhoused people, sex workers, queer communities, and undocumented immigrants. Keeping in mind the ethical issues that arise when using data – and considering the possibilities of self-determination to build trust between different stakeholders while incorporating civic engagement into this agenda – this paper presents an applied approach to urban data policies for the 24-hour city. The three main questions are 1) How does the lack of access to consistent data about the nighttime ecosystem affect policymaking, urban governance, and citizen-centred data interactions?; 2) What are the harmful practices in data collection, publishing and assessment concerning digital rights and who are they harming?; 3) How would an agenda for responsible, trustful and ethical engagement with policy data interactions for the night look like? This paper is based on both a three-year applied research project that aimed to understand responsible data science practices for the night in Montreal (Canada) and two years of experience as a member of the MTL 24/24 Night Council in the same city.
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Prédiction distillée sur la base complète
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,006 | 0,013 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
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