Case Study on Competition Law Enforcement Against Database Restrictions: Focusing on IP Guideline and TREB Ruling in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a data-driven economy, companies (esp. platform companies) with large amounts of data can use it to gain competitive advantages, such as product and service innovation and improvements. However, when big data is concentrated in the hands of a few companies, new entrants may find it difficult to enter the market or, if they do, to compete. It is legal for companies to collect significant amounts of data and to manage and control it. However, the issue of misuse of data to increase entry costs and extend market power is being debated in various countries around the world on the need for regulation at the competition law. This study will introduce the Canadian Competition Bureau's Intellectual Property Enforcement Guidelines (the “IP Guidelines”) and the case of the Toronto Real Estate Board's (the “TREB”) restrictions on the use of a real estate database in the Toronto area, which addressed whether the restrictions constituted an abuse of market power under Canadian competition law(the “Case”). The case has been a long-running legal battle that began in 2011 with an investigation by the Canadian Competition Bureau and concluded in 2018 with a decision by the Supreme Court of Canada. As such, it is considered to be a precedent-setting case in the context of determining abuses of market power in relation to database restrictions. Recently, the Korea Fair Trade Commission also imposed a corrective order and a fine on NAVER for the act of preventing a third party (a competitor such as Kakao) from providing real estate listing information provided by NAVER through a contract with a real estate information provider. The case contains many similar legal issues to the NAVER case and is worthy of comparison and analysis. This study draws implications for Korean competition law enforcement through foreign regulations and case studies on platform operators' data access restriction.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 it