Measuring Vertical Integration in the Technology Sector: Indonesia, the US, and the EU in Unfair Competition
Bibliographic record
Abstract
On several occasions, Indonesian competition authorities have attempted to apply Article 14 to adjudicate violations related to vertical integration practices; however, these attempts were invalidated at the objection and cassation levels. The criteria utilized include the concepts of unfair business competition and public harm, as these terms are instrumental in determining the impacts of violations concerning vertical integration. This research aims to examine the legal approaches employed by Indonesian competition authorities in addressing vertical integration, with a particular focus on the technology sector. The findings indicate that the criteria for assessing whether vertical integration constitutes a violation of unfair business competition vary among Indonesia, the United States, and the European Union. In Indonesia, the emphasis is on preventing the exclusion of access to essential raw materials or significant buyers, utilizing the Rule of Reason approach. In contrast, the U.S. evaluates public detriment by balancing fairness and competitive benefits, whereas the EU focuses on market dominance and its potential to reduce competition. Despite these variations, all three jurisdictions share a common objective of enhancing consumer welfare and promoting competitive market conditions, with specific regard to differing regulations on online sales restrictions.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".