Bibliographic record
Abstract
The present study aims to make comparative analysis of competition law in Pakistan and China by analyzing the leniency programs that whether or not they are in accordance with market structure or not, and investigating the mechanism to evidences while applying leniency policies and its value in competition law. The study adopts qualitative data analysis in order to analyze the respective aims and objective. It is found out by this research that progressive and unconventional are very important to be taken by both countries in order to ingeniously enforce competition law. Although competition law is supposed to prevent anti competition rituals and practices by nurturing free and fair competition in the market. It promotes a greater competition in the market by safeguarding customers against inaccurate means, which are adopted by firms. Therefore, competition law can be regarded as highly essential for regulating businesses by ensuring producer and consumer welfare. It ultimately promotes healthy growth of the economy and social justice. While on the other hand, a huge budget is entailed by investigation procedures which have been regarded as a huge financial resources’ loss by experts. In addition to this, there is also a greater risk of surcharges of violation, punishment and legal costs, which sometimes lead to harm corporate image. Moreover, the leniency programs in both Pakistan and China cover administrative liability only. Therefore, it is important to voluntarily comply with competition rules, regulations and laws, which would play an immensely significant role in minimizing the social costs which occur due to this law enforcement. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".