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 24-5 259
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.726 | 0.536 |
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".