Bibliographic record
Abstract
On a “marketplace ” platform, where two sides of users trade, the platform owner has an incentive to regulate its marketplace for a higher profit. This study focuses on a monopoly platform’s nonpricing, regulatory strategies in governing quality heterogeneity of competing sellers. In contrast to related studies, we endogenize strategic interactions among platform users. Our model extends the circular city model to capture seller heterogeneity in both variety and quality. The closed-form equilibrium solution reveals a ripple effect that exerts competitive pressure from seller to seller at a diminishing magnitude. The equilibrium analysis enables us to connect the economic mechanisms in users ’ trading strategies with the platform’s regulatory problem. We find that the platform does not benefit from an equal support to all sellers that increases the average quality. Instead, the platform owner is better off providing discriminatory support in favor of higher-quality sellers to enhance quality heterogeneity. Moreover, the optimal quality support rate is lower for a higher average of seller quality because quality support is more costly; on the other hand, a higher variance make quality levels more responsive to discriminatory support and leads to a higher support rate. A higher transportation cost for
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".