Future-proofing the city: A human rights-based approach to governing algorithmic, biometric and smart city technologies
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.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".