Bibliographic record
Abstract
New data-driven technologies in global cities have yielded potential but also have intensified techno-political concerns. Consequently, in recent years, several declarations/manifestos have emerged across the world claiming to protect citizens’ digital rights. In 2018, Barcelona, Amsterdam, and NYC city councils formed the Cities’ Coalition for Digital Rights (CCDR), an international alliance of global People-Centered Smart Cities—currently encompassing 49 cities worldwide—to promote citizens’ digital rights on a global scale. People-centered smart cities programme is the strategic flagship programme by UN-Habitat that explicitly advocates the CCDR as an institutionally innovative and strategic city-network to attain policy experimentation and sustainable urban development. Against this backdrop and being inspired by the popular quote by Hannah Arendt on “the right to have rights”, this article aims to explore what “digital rights” may currently mean within a sample consisting of 13 CCDR global people-centered smart cities: Barcelona, Amsterdam, NYC, Long Beach, Toronto, Porto, London, Vienna, Milan, Los Angeles, Portland, San Antonio, and Glasgow. Particularly, this article examines the (i) understanding and the (ii) prioritisation of digital rights in 13 cities through a semi-structured questionnaire by gathering 13 CCDR city representatives/strategists’ responses. These preliminary findings reveal not only distinct strategies but also common policy patterns.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".