Bibliographic record
Abstract
abuse of dominance Czech Republic 74-5 Hungary 76 innovation, IP and competition policy and 127 Poland 78 restrictive agreements and 155, 157, 159-60, 162-3, 171 see also dominance access to data 26, 31, 35-8 administrative process efficiency/effectiveness conflict 218-26 failure conflict 226-31 structural conflict 231-5 see also judicial process agreement horizontal 74, 145, 158-9 single-branding 157, 158-9, 159-60, 166, 167 vertical 74, 76 allocation theory 96 antitrust data control by US lawyers and 39 data interpretation by US economists and 41-2 law 21, 22-3, 34-5 , 59, 140-41, 145, 146-7 Antitrust Modernization Commission (AMC) 227-8 assessment ex-ante, uncertainty in 195-6 state-aid control and 17-18 AstraZeneca 15-16 behaviour defendant's future, France 250-51 economic concept of 214-16 positive economic model of 71-2 see also conduct Bertelsmann/Springer/JV 10-11 Blackstone/Acetex 13-14 Boeing/McDonnell Douglas 54 Böhm, Franz 115 Britain see United Kingdom bundling restrictive agreements and unilateral restraints and 171 unilateral conduct rules and 172-4 see also tying burden of proof restrictive agreements and unilateral restraints and 165 South Africa 82, 85 see also proof Canada 13-14, 83, 86 cartel Czech Republic 73-5, 78 economic analysis 16 exploitative practices 177 Hungary 75 Japan 57, 61-2, 66, 67, 68 Poland 77 restrictive agreements and unilateral restraints and 156, 166 cease-and-desist order, Japan 66 certainty, legal, as administrative costs limitation, competition law goals and 107-8 see also uncertainty civil procedure, basic principles, US antitrust experience 33-5 collective boycott 156 common law litigation model 30-31 competition 'on the merits' 115-16 'perfect' 96, 97, 99, 111 Competition Appeal Court (South Africa) 81 Competition Commission (South Africa) 81, 82 competition law economics in, use of 24-5
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.033 |
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; both teacher heads agree on what is shown here.
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".