Bibliographic record
Abstract
Prevailing tests for whether bundled rebate programs are anticompetitive, including the recent Antitrust Modernization Commission Recommendation 17, are based on whether some incremental or total price in the rebate program is less than some appropriate incremental cost. This test presumes that rebate programs, and exclusionary conduct more generally, should be treated like predation cases. It errs in treating the buyers as end users rather than competing complement providers, as they are in all of the leading U.S. and Canadian cases. Rebate programs should be assessed on the basis of whether they raise the price of a complement, such as retailing or distribution. This suggests a different two-prong test: Does the rebate cover a competitively significant share of a complement market? If so, what effect does the rebate have on the price that rivals have to pay to obtain the complement? This test allows the use of merger guideline approaches, ignores (for the most part) cost comparisons, and does not require prior dominance in the primary market. An assessment of this approach examines when practices are exclusionary, compares rebates to exclusive dealing, distinguishes exclusionary from predatory rebates, critiques “profit sacrifice” approaches to exclusion, and proposes share-based remedies to recognize vertical efficiencies.
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 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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".