Bibliographic record
Abstract
Economics rightfully represents the major basis for competition policy. Next to generating knowledge about competition and its welfare effects, the currently popular 'more-economic approach' is charged with a number of additional hopes and expectations, leading to a reduction of the ambiguities of real-world competition policy. While this article highlights the benefits of economics-based competition policy, it takes a cautious stance towards excessive expectations in particular regarding the idea that a monocultural, 'unified' competition theory as an exact, objective, and unerring scientific approach to antitrust makes normative assessment and generalizations superfluous. In a combination of two lines of argumentation, diversity in competition economics is advocated. Firstly, competition economics is empirically characterized by a considerable pluralism of theories and policy paradigms. This includes deviating views on core concepts like the nature of competition, the meaning of efficiency, or the goals of antitrust. Secondly, it is demonstrated that diversity of theories represents no imperfection of the state of science. In contrast, it is theoretically beneficial for future scientific progress. Therefore, no ultimate competition theory can ever be expected. As a consequence, the 'more-economic approach' must be extended in order to embrace diversity. This does not decrease its meaning and importance but instead puts some of the related high hopes into perspective.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".