First-Party Content and Coordination in Two-Sided Markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The strategic use of first-party content by two-sided platforms is driven by two key factors: the nature of buyer and seller expectations (favorable versus unfavorable) and the nature of the relationship between first-party content and third-party content (complements or substitutes). Platforms facing unfavorable expectations face an additional constraint: their prices and first-party content investment need to be such that low (zero) participation equilibria are eliminated. This additional constraint typically leads them to invest more (less) in first-party content relative to platforms facing favorable expectations when first- and third-party content are substitutes (complements). These results hold with both simultaneous and sequential entry of the two sides. With two competing platforms—incumbent facing favorable expectations and entrant facing unfavorable expectations—and multi-homing on one side of the market, the incumbent always invests (weakly) more in first-party content relative to the case in which it is a monopolist. This paper was accepted by Bruno Cassiman, business strategy.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| 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 it