Bibliographic record
Abstract
This chapter examines what the rise in use of artificial intelligence (AI) may mean for Canadian competition law. Using the recent Consent Agreement entered into between Facebook and the Commissioner of Competition as a foil, the chapter explores the limits of current rules to respond to the kinds of anticompetitive effects that may plausibly flow from commercial uses of artificial intelligence, particularly by large firms with the power and resources to collect, consolidate, and control the most valuable resource in today’s digital world—information. It is important to consider how these changes will play out within the specific ambit of the Competition Act and Canadian competition policy, as these are informed by the nature of the Canadian economy structurally, as well as Canada’s place in the world economic order as a small trade-dependant nation sitting in geographical proximity to a powerful economic neighbour. While the nature of AI and the speed with which it continues to evolve makes it hard to know exactly how business practices which make use of AI will influence competition analysis and enforcement activity, this chapter assessment indicates there are gaps to be filled as Canada’s competition policy is adapted to the realities of the digital age.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".