Comparative Study of Competition Law between China and Pakistan with Special Reference to the Use of Evidences Submitted by Companies to Other Legal Proceedings
Bibliographic record
Abstract
The present study makes an attempt to make comparison between China and Pakistan with reference to Competition law. The research aims to find out that whether or not the evidences submitted by the companies during the course investigation can substantially be used in any other legal proceeding. As far as the methodology of this study is concerned, qualitative data analysis is used along with comparative legal method for analyzing “de lege lata” and “de lege ferenda” situation in scope of the solved topic. The study finds out that competition in Pakistan works same as China’s AML since both forbids actions that play their negative role in reducing the competition like market dominance in the market. Therefore, the act encourages agreements that confine and restrict market dominance. Furthermore, methods and policies are stated by the law with reference to review of enquiries, acquisitions, mergers, penalties’ imposition, leniency’s grant along with other aspects of law enforcement. The evidences submitted by the companies during the course investigation can substantially be used in any other legal proceeding. The study concluded while contending that, however, AML in China and competition Act in Pakistan has provided both countries substantive and sound law, but there is need of strong and effective institutional implement which can provide a base for the evidences submitted by the companies during the course investigation to be substantially used in any other legal proceeding. Compliance is promoted by leniency through competition law along with incentives to prohibited arrangements. Qualitative research methodology has been applied to the following article.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".