On the concepts of legal standards and substantive standards (and how the latter influences the choice of the former)
Bibliographic record
Abstract
Abstract The substantial literature on the optimal choice of legal standards (LSs) in Competition Law enforcement concentrates on the factors that influence this choice given the Substantive (or Liability) Standard adopted by courts and competition authorities (CAs). Generally, this literature assumes that the substantive standard (SS) is welfarist. However, in reality, courts and CAs in different countries and over time use different criteria for establishing liability and, very often, these criteria are not welfarist. This article’s main objective is to clarify the relationship between legal and SSs and show the important influence of the latter on the choice of the former: our analysis shows that while efects-based LSs are compatible with non-welfarist SSs, under the latter courts and CAs will be much more likely to use Per Se LSs. This occurs as under non-welfarist SSs the strength of the presumption of illegality will be higher. This influence may be considered as being mainly responsible for differences in the LSs adopted in European Union and in North America (USA and Canada) or UK, especially in relation to abuse of dominance cases.
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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 | 0.033 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".