Municipal governance of the sharing economy sectors: global insights
Bibliographic record
Abstract
The sharing economy generates many benefits for cities and their citizens. However, it also causes numerous economic, social and environmental challenges, which are the reasons cities develop various policies and regulations. Previous research has explored municipal regulations across cities and countries but often overlooked alternative governance mechanisms. Our earlier work highlighted the need to systematically explore variations in governance approaches adopted by municipalities towards the sharing economy's sectors like accommodation, mobility and physical goods. This chapter compares municipal governance in ten cities, Amsterdam, Berlin, Gothenburg, London, Malmö, Melbourne, San Francisco, Seoul, Shanghai and Toronto, according to a comprehensive framework of municipal governance comprising five mechanisms and 11 roles. Empirical evidence comes from 212 semi-structured interviews, ten mobile research labs, nine stakeholder workshops, three focus groups, and a review of academic and grey literature. This chapter unpacks why municipal governance of the sharing economy varies across cities and sectors, introducing a framework for categorising contextual factors shaping municipal engagement with sharing organisations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".