Recent Legal and Regulatory Developments in Slotting Allowances and Category Management
Bibliographic record
Abstract
DAVID BALTO is a partner, White and Case L.L.P. He is the former head of the Policy Office, Bureau of Competition, Federal Trade Commission. Slotting allowances and category management are increasingly the subject of antitrust litigation and antitrust enforcement actions. In the past two years, slotting allowances received greater attention before the Federal Trade Commission (FTC) and Canadian Competition Bureau (CCB), in terms of both distribution practices and mergers. Several cases are in litigation, and in Spring 2002, two important decisions were handed down that begin to illuminate the legal rules addressing slotting allowances. The controversy over slotting allowances appears as vibrant as it has for the past few years, with increasing attention likely in the future. What were the major recent developments involving slotting allowances and category management?
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".