The Revival of Fairness Discourse in Competition Policy
Bibliographic record
Abstract
The resurrection of the free trade–fair trade debate at the international level parallels a renewed pressure to incorporate fairness considerations within domestic competition policies, originating from a perceived reduction in the level of competition in various industries. Both supporters and critics of notions of fairness, however, have failed to advance a normatively persuasive case in their favor, neglecting the multifaceted nature of fairness in the competition policy domain. In this article, we disaggregate and evaluate four specific notions of fairness that are pertinent to domestic markets: vertical fairness between producers and consumers, horizontal fairness on the demand side, horizontal fairness on the supply side, and procedural fairness. We defend a narrow dimension of horizontal fairness on the supply side consistent with efficiency goals and of procedural fairness in the enforcement of competition laws. We conclude by comparing the free trade–fair trade debate with the return of fairness in competition policy.
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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 | 0.052 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".