Beyond digital access as a human right in cities: proposing an integrated, multi-dimensional approach
Bibliographic record
Abstract
An estimated 68% of the world’s population is expected to live in cities by 20501. The rapid development of emerging technologies, pressures from global crises, and the introduction of "Smart City" strategies have resulted in an acceleration of urban digital transformation. While high internet access and mobile network coverage rates may leave us with an impression that many cities have achieved universe digital access, these statistics only tell part of the story. The COVID-19 pandemic, climate and political crises, conflicts, forced displacement, and deepening economic inequality have all exacerbated and further amplified existing digital divides, rendering access more difficult for systematically marginalised and excluded communities. Urban digital divides and access issues undermine sustainable development, including UN Sustainable Development Goal 11, which aims to "make cities and human settlements inclusive, safe, resilient and sustainable". A lack of universal, comprehensive digital access can prevent people from accessing information, healthcare, education, social services, employment, governance forums, and public space. It is important for cities to take a critical approach to urban digital transformation to avoid techno-solutionism, algorithmic bias, tech-facilitated rights violations and surveillance, digital divides and apartheids, and disinformation. While some jurisdictions have formalised internet access as a legal right, digital divides and access issues continue to persist at the local level. The United Nations University Operating Unit on Policy-Driven Electronic Governance (UNU-EGOV) and Digital Future Society (DFS), as part of a shared commitment to promoting a deeper understanding of the intersection of technology and society, have co-developed the following whitepaper as a contribution to the work of the SDG11 Global Council. Six cities — Barcelona, Johannesburg, Mexico City, Riga, Singapore, and Toronto — were selected as case studies, representing diverse geographic contexts, to highlight both challenges and opportunities for achieving meaningful, universal digital access in cities. While some cities are beginning to adopt people-centred and human rights-based approaches, these approaches need to be complimented by a more comprehensive, systematic approach to digital access that prioritises the wellbeing, rights, and agency of people, communities, and the environment to help ensure cities do not widen digital divides. The paper advocates for the adoption of an intersectional, human rights-based, systems approach and includes a list of recommended actions for city officials, policymakers, and community leaders to realise digital access and SDG 11 (making cities inclusive, safe, resilient, and sustainable). Overall, the paper aims to guide local governments and civil society in how they can approach their work towards realising SDG 11 in tandem with facilitating more equitable and just urban digital transformations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".