ABUSE OF JOINT DOMINANCE IN CANADIAN COMPETITION POLICY
Bibliographic record
Abstract
The Canadian Competition Bureau has recently offered new draft guidelines on the abuse of dominance that, in the area of joint dominance, depart from the existing guidelines in two ways: first, the bureau no longer considers as a potential abuse of joint dominance the adoption of practices that facilitate supra-competitive pricing in an oligopoly; second, while in the past some form of explicit coordination was required for an assessment of joint dominance, the bureau now considers parallel abusive conduct by jointly dominant firms as potentially infringing the abuse provisions. The first change, which we attribute to case law rather than to the bureau, is undesirable. The adoption of facilitating practices can lessen competition, and is practically remediable. Facilitating practices should be considered potential abuses of joint dominance. On the other hand, the second change is sensible: oligopolists may profitably adopt exclusionary practices in parallel without coordination. Parallel exclusionary practices may lessen competition even when no single firm has a dominant market share, and this problem is amenable to a practical remedy. The bureau's new approach is welcome on this front.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".