Canada's Efficiency Defence: Why Ignoring Section 96 Does More Harm Than Good for Economic Efficiency and Innovation
Bibliographic record
Abstract
Canada has a defence that allows efficiency enhancing mergers and collaborations between competitors. In another paper published in the same edition of the Canadian Competition Law Review, Chiasson and Johnson argue that the efficiencies defence should be repealed because it reduces innovation and causes inefficiencies. In our view, Chiasson and Johnson take an overly simplistic view of the relationship between market concentration and innovation that misses a fundamental point: mergers between competitors often increase efficiency and innovation. We also argue that efficiencies are not given enough weight and anticompetitive effects are overemphasized under the Competition Bureau’s approach to merger review, which creates a bias against efficiency enhancing mergers. Removing this bias would help the Competition Act function as Parliament intended. In the words of the Supreme Court of Canada: “the efficiencies defence is Parliamentary recognition that, in some cases, consolidation is more beneficial than competition.”
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".