Data for the Night: Digital Rights, Trust, and Responsible Engagement with Data in 24-Hour Cities
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".