Robots, Regulation, and the Changing Nature of Public Space
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
Robots are an increasingly common feature in public spaces. From regulations permitting broader drone use in public airspace, and autonomous vehicle testing on public roads, to laws permitting or restricting the presence of delivery robots on sidewalks – law often precipitates the introduction of new robotic systems into shared spaces. Laws that permit, regulate, or prohibit robotic systems in public spaces will in many ways determine how this new technology affects 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? With a focus on the Canadian legal system, and drawing upon insights from the interdisciplinary field of law and geography, this chapter 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 this reason, 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 avoid permitting 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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