Patterns of Local Policy Disruption: Regulatory Responses to Uber in Ten North American Cities
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Since its inception in 2009, Uber has grown into a technology behemoth, with operations in over 70 countries and 500 cities around the world. Along the way, it has successfully forced regulatory upheaval in hundreds of local taxi markets controlled by municipal authorities. In this sense, Uber is not only a market disruptor, but also a policy disruptor. This paper examines the nature of such policy disruption at the local level by reviewing regulatory responses to Uber in ten North American cities. We find that regulatory outcomes are a function of two factors: Uber’s government relations strategy, either cooperative or confrontational, and the degree to which local governments perceive Uber as complementary or harmful to the existing marketplace. We conclude by proposing a typology of regulatory responses to Uber as a basis to identify patterns in the behavior of municipal regulatory authorities and political leaders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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