The Rule of Reason Approach in Discriminatory Practices: Airlines and Telecommunications Industry Sector
Bibliographic record
Abstract
Discriminatory practices are standard in business competition and are not prohibited as long as they do not cause unfair competition. This paper will discuss three Business Competition Supervisory Commission (KPPU) decisions in 2020 related to alleged discriminatory practices committed by business actors. The subject matter in this paper is how the actions of business actors can fulfil the elements of violation and how the application of the rule of reason approach in Article 19 letter d of the Competition Law (1999) in the 2020 KPPU Decisions. This research is descriptive normative research. The data used in the book, articles, the new paper analyzed the Competition Law (1999), the Airlanes law (2009), the Electronic Information and Transactions Law (2008, amendments 2016 & 2024), and the Hajj and Umarah Law (2019), as well as interview an expert and KPPU. The interesting findings found that acts of discrimination cause obstacles in vertical business relations in different but interrelated relevant markets and often occur in the essential facilities sector. By using the rule of reason approach, KPPU found that discriminatory practices will be more effective if the business actor is in a dominant position or even occupies a monopoly position. Then, the three decisions in this discussion are equally suspected of violating Article 19 letter d on discriminatory practices. Then, related to the relevant market, the three cases have different markets, and also all three have vertical relationships with other business actors.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".