Bibliographic record
Abstract
When Melanie Aitken, who retired as Canada’s Commissioner of Competition effective September 21, 2012, was appointed to the post in 2009, she announced that the hallmark of her tenure would be enhanced enforcement efforts. She did not disappoint on that score. Ms. Aitken advanced cases against Canada’s largest telecommunications carriers;1 settled a civil advertising case for a seven figure amount;2 brought cases to the Competition Tribunal for abuse of dominance in the waste disposal3 and real estate brokerage businesses;4 fought a contested case before the Competition Tribunal against Visa and MasterCard under Canada’s new price maintenance provisions;5 commenced a contested abuse of dominance case involving the Toronto Real Estate Board;6 tested the new civil provisions of the Competition Act with respect to agreements amongst competitors;7 brought the first prosecution for breach of a consent agreement;8 and has prosecuted numerous cartels, including those involving retail gasoline.9 Canada is expected to remain a jurisdiction notable for vigorous competition law enforcement, with Ms. Aitken’s successor, Commissioner of Competition John Pecman, confirming on multiple occasions that the Bureau’s active enforcement will not abate under his leadership.10
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.628 | 0.316 |
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".