Future-proofing the city: A human rights-based approach to governing algorithmic, biometric and smart city technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While the GDPR and other EU laws seek to mitigate a range of potential harms associated with smart cities, the compliance with and enforceability of these regulations remain an issue. In addition, these proposed regulations do not sufficiently address the collective harms associated with the deployment of biometric technologies and artificial intelligence. Another relevant question is whether the initiatives put forward to secure fundamental human rights in the digital realm account for the issues brought on by the deployment of technologies in city spaces. In this special issue, we employ the smart city notion as a point of connection for interdisciplinary research on the human rights implications of the algorithmic, biometric and smart city technologies and the policy responses to them. The articles included in the special issue analyse the latest European regulations as well as soft law, and the policy frameworks that are currently at work in the regions where the GDPR does not apply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it