Bibliographic record
Abstract
Robots are an increasingly common feature in North American public spaces. From regulations permitting broader drone use in public airspace and autonomous vehicle testing on public roads, to delivery robots roaming sidewalks in major U.S. cities, to the announcement of Sidewalk Toronto – a plan to convert waterfront space in one of North America’s largest cities into a robotics-filled smart community – the laws regulating North American public spaces are opening up to robots. In many of these examples, the growing presence of robots in public space is associated with opportunities to improve human lives through intelligent urban design, environmental efficiency, and greater transportation accessibility. However, the introduction of robots into public space has also raised concerns about, for example, the commercialization of these spaces by the companies that deploy robots; increasing surveillance that will negatively impact physical and data privacy; or the potential marginalization or exclusion of some members of society in favour of those who can pay to access, use, or support the new technologies available in these spaces. The laws that permit, regulate, or prohibit robotic systems in public spaces will in many ways determine how this new technology impacts public space and the people who inhabit that space. This begs the questions: how should regulators approach the task of regulating robots in public spaces? And should any special considerations apply to the regulation of robots because of the public nature of the spaces they occupy? This paper argues that the laws that regulate robots deployed in public space will affect the public nature of that space, potentially to the benefit of some human inhabitants of the space over others. For these reasons, special considerations should apply to the regulation of robots that will operate in public space. In particular, the entry of a robotic system into a public space should never be prioritized over communal access to and use of that space by people. And, where a robotic system serves to make a space more accessible, lawmakers should be cautious to avoid providing differential access to that space through the regulation of that robotic system.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".