Anti-Dumping and Anti-Trust Issues in Free-Trade Areas Revisited: A Comment on <i>AfCFTA</i> ’s <i>Protocol on Competition</i>
Bibliographic record
Abstract
Gabrielle Marceau devoted her Ph.D. thesis to the interaction between anti-dumping and anti-trust policies within free trade areas. The conclusions acknowledge that a robust and coordinated competition policy in both importing and exporting countries is crucial for avoiding the abuse of anti-dumping procedures and reaping the rewards of economic integration. Building on this premise, this article examines the use of competition policy for greater economic integration within regional trade agreements. To fully benefit from the integration process, the institutional framework and enforcement mechanisms of competition regimes must be tailored to specific policy objectives of the economic partnership. The design of the regional competition regulation and enforcement therefore matters. Taking a specific focus on the African Continental Free Trade Area’s Protocol on Competition (AfCFTA) this article offers a tailored approach to assessing its institutional design features and enforcement mechanisms. It draws lessons from those competition regimes embedded in other mega-regional RTAs and evaluates the Protocol on Competition ’s general framework design in terms of AfCFTA ’s policy objectives. The Protocol puts forward a multilayered approach to competition law enforcement with various national, regional, and supra-national actors addressing anti-competitive practices that fall within their respective jurisdictions. While this design responds to key challenges in establishing a continent-wide competition regime, the article concludes by identifying further focus areas for the regime’s implementation, with an eye to facilitating deeper economic integration.
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 | 0.051 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.098 | 0.085 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".