Bibliographic record
Abstract
In this paper, I analyze the welfare effect of a vertically integrated gatekeeper platform selling its own first-party product, i.e., first-party selling, as well as the platform's incentive to favor the first-party product in the product recommendations it makes, i.e., self-preferencing. I find that, irrespective of self-preferencing, both consumer welfare and platform revenue are higher under first-party selling because first-party selling mitigates double marginalization. Additionally, third-party product prices are lower in expected terms under first-party selling, either because the platform reduces the commission fee (with self-preferencing) or downstream competition is fiercer (without self-preferencing). Finally, I show that both consumers and the platform are better off if the platform commits not to engage in self-preferencing. • A platform can sell its own (1P) product in the marketplace (1P selling). • The platform may recommend the 1P product favorably to consumers (self-preferencing). • 1P selling reduces double marginalization and increases welfare. • The platform's revenue and consumer welfare are higher without self-preferencing. • If the platform has commitment power, it does not engage in self-preferencing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".