On the Illegality and Regulation of Algorithmic Price Discrimination in China's Digital Economy
Bibliographic record
Abstract
The legal issue of algorithmic price discrimination sparked by the in-depth use of big data and algorithm techniques has emerged as a significant concern in the development of China's digital economy. Although Chinese law has implemented many regulations on the collection and protection of personal information, data security and governance, as well as on price discrimination, instances of algorithmic price discrimination have arisen in judicial practice. The legal issue surrounding algorithmic price discrimination has not yet been fully resolved. Legal studies in China on this issue mainly uses "big data killing" or "algorithmic price discrimination" to define it. Regarding the legal classification and regulation of algorithmic price discrimination: the Anti-Monopoly Law's regulatory measures are limited from a competition law standpoint. Instead, Anti-Unfair Competition Law provides a more appropriate framework. As for civil law, the question of whether the algorithmic price discrimination qualifies as a civil tort still requires discussion; nevertheless, it satisfies all the constitutive elements of fraud in Chinese civil law.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".