Merger policy for a dynamic and digital Canadian economy
Bibliographic record
Abstract
As an element of competition and antitrust law frameworks, merger policy plays an important role in preventing acquisitions that would otherwise allow incumbent firms to extinguish competitive threats and entrench their dominance. But evidence suggests that current approaches to merger law in Canada and abroad have underestimated the harms these transactions can pose to competition and overestimated the effectiveness of the remedies intended to mitigate those harms. Although relevant across the Canadian economy, this permissive treatment of mergers is particularly pronounced in digital markets, where platform business models, the importance of potential competitors and the role of intangible assets such as data as a barrier to entry test the assumptions underlying the country's merger law. Canada's current law and jurisprudence mean the Competition Bureau, Canada's sole competition authority, is limited in its ability to detect potentially harmful transactions, faces material barriers to intervening and fully remedying the harms of those transactions, and is unable to assess the outcome of previous action or inaction. This paper provides recommendations for Canada to ensure its merger law is calibrated for a modern economy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".