The diffusion of the sandbox approach to disruptive innovation and its limitations
Bibliographic record
Abstract
Faced with the challenges posed by the rise and evolution of disruptive technologies and innovations, many countries have adopted differing regulatory approaches and adapted institutional structures and norms to maximize benefits while mitigating risks. Among such regulatory endeavors, the regulatory sandbox, first adopted by the UK for the financial sector, stands out as a prominent mechanism to strike a balance between promoting technological innovations and ensuring market order. Given the promises of the regulatory sandbox, there has been a gradual embrace of this approach by governments across continents, which arguably indicates a global norm diffusion is on the rise. There is also a trans-governmental endeavor to facilitate cooperation among regulators and convergence in regulation through bilateral arrangements, as well as under the multilateral “global sandbox” club. Beyond the financial sector, given the cross-border nature and implications of many disruptive technologies and innovations, some countries have moved forward and applied similar governance approaches to non-financial areas, and this paper has discussed examples of them in Canada, Japan, Singapore, and Taiwan in areas such as energy, the environment, health care, and transportation. All these developments evidence the rise of the sandbox approach to regulating disruptive technologies and innovations in different sectors at the national, trans-governmental, and global levels, which has crucial theoretical and practical implications. By way of an in-depth and thorough analysis on Taiwan’s aggressive use of sandbox regulation in the areas of financial services, unmanned vehicles, and more recently, artificial intelligence, this paper argues that while the sandbox approach has emerged as a handy tool for governments to manage the ramifications across different sectors and despite its global diffusion over the past few years, there are limitations that may affect how countries implement these regulatory approaches on the ground. As argued in the case of Taiwan, the legal system, regulatory culture, domestic political economy in the post-GFC era all play a crucial role in shaping path dependence and institutional inertia nested within regulatory agencies. One must not take the face value of the sandbox approach; it is those complicated local contexts, seen or embedded, that will define the ultimate contour of the global sandbox approach in the long run.
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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 | 0.031 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".