Strategies for Two Sided Markets
Bibliographic record
Abstract
When markets have different types of users that attract one another, more demand from one group can spur additional demand from the matched group in a virtuous cycle. These effects are often present in markets. Examples include application developers/users, credit card merchants/cardholders, and dating sites for men/women. Network effects in these two-sided markets significantly affect prices, competition, and industry concentration. Prices generally fall for the group that is the stronger attractor. Winner-take-all markets can arise depending on the strength of these network effects, economies of supply, specialization, and multihoming. Finally, when users of one overlap users of another, there is an opportunity for platform envelopment where one swallows a competitor. Bundling features that attract one group of the competitor's users can bring along the matching group.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".