Horizontal Collusions Organized by Uber: Time for a Change in Canada
Bibliographic record
Abstract
This paper argues that Uber’s ordinary operation should be characterized as organizing horizontal cartels among drivers that not only fix the fares of ride- hailing services using its platform but also allocate customers. Uber-led cartels, therefore, violate section 45(1) of the Competition Act5 of Canada. In doing so, this paper analyzes the relationships between Uber and drivers and argues that (i) Uber is the organizer of price-fixing and market allocation collusions among drivers, (ii) the collusions are horizontal, and (iii) they are per se illegal.\nThe first section discusses the general structure of peer-to-peer markets. The second section examines factors indicating the illegality of cartels organized by Uber under the Competition Act of Canada. The last section will provide some legal and technical solutions that may help Uber to remedy the anti- competitiveness of its platform.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".