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
This is an era of information that is moving towards digitalization, and the term "artificial intelligence" is no longer unfamiliar to contemporary young people. Robots are able to simulate human movements, this deep development of science and technology has undoubtedly profoundly changed our way of production and life. Artificial intelligence has gradually penetrated into the development of criminal law in China, bringing considerable risks and challenges to traditional criminal law and criminal proceedings. We are still in the era of weak artificial intelligence and will be in the era of weak artificial intelligence for a long time, and its "tool attribute" is undeniable. This paper is committed to analyzing the risks of the integration of artificial intelligence technology and traditional trial mode, the risks of the increase of artificial intelligence crimes and the criminal legal risks arising from the difficulty of criminal attribution, then puts forward several suggestions on the subject status, technical prevention , and legal regulation of weak artificial intelligence. The purpose is to foresee the risk as early as possible, let artificial intelligence better serve mankind, and make technology and law together to promote the process of China's rule of law.
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 | 0.012 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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