Bibliographic record
Abstract
A striking diversity of competition law institutions exists around the world. There are three basic institutional models: (1) the bifurcated judicial model, in which specialised investigative and enforcement authorities bring formal complaints before the courts; (2) the bifurcated agency model, in which specialised investigative and enforcement agencies bring formal complaints before separate, specialised adjudicative agencies; and (3) the integrated agency model in which a single specialised agency undertakes investigative, enforcement and adjudicative activities. Institutions may also combine features of the three models, but this article focuses on the three models as useful points of reference. This article conducts a preliminary evaluation of the advantages and disadvantages of each of these models against a set of normative criteria identified at the outset of the article, including such considerations as independence, accountability, predictability and flexibility. As these considerations suggest, important procedural values often will be in tension with one another. The article evaluates the tendencies of each of the models to vindicate the different normative criteria. It then considers the role of political appeals from adjudicative decisions. Finally, the article considers the political economy of the choice of institutional arrangement. While the article considers international experience, it draws primarily on Canadian experience.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".