Addressing the Challenges of Government Service Provision with Artificial Intelligence
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
In complete contract theory, the main approach to limit moral hazard is through modifying incentives for the agents. However, such modifications are not always feasible. One prominent example is Chinese government service provision. Over the years, it has been plagued with inefficiencies as a result of moral hazard. Previous attempts to address these challenges are not effective, as reforms on civil servant incentives face stiff hindrance. In this article, we report an alternative platform — SmartHS — to address these challenges in China without modifying incentives. Through dynamic teamwork, automation of key steps involved in service provision, and improved transparency with the help of artificial intelligence, it places civil servants into an environment that promotes efficiency and reduces the opportunities for moral hazard. Deployment tests in the field of social insurance service provision in three Chinese cities involving close to 3 million social insurance service cases per year demonstrated that the proposed approach significantly reduces moral hazard symptoms. The findings are useful for informing current policy discussions on government reform in China and have the potential to address long‐standing problems in government service provision to benefit almost one‐fifth of the world's population.
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 | 0.000 |
| 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