Bibliographic record
Abstract
Online platforms of digitized media content face different challenges on managing the consumer demand and creator production compared to the traditional media industry. This work utilizes data from a major online book platform in China to investigate these issues and provides insights on platform management regarding content releases and creator incentives. The first essay studies the impact of the digital platform’s product release strategy of serialized media content on its consumption and the ensuing profits made by the platform. Specifically, should a platform release all chapters of a book simultaneously, sequentially over time, or with a hybrid strategy where it releases a fraction simultaneously and the rest sequentially? I show that the release speed of chapters impacts consumption through two opposite forces: binge consumption and product exploration. A slower release, while limiting binge consumption, leads to an increased exploration of other books through two mechanisms - consumers visit the platform more often and explore other books due to the constrained availability. The counterfactuals show that the platform will be worse-off under a simultaneous release strategy but better-off under an optimized hybrid release strategy. The second essay focuses on how the platform should design a monetary contract to incentivize content creators to produce higher quantity and higher quality works. The platform switched from a uniform commission rate to a quantity-based plan offering higher commission rates if a writer's production meets higher quantity brackets. Theoretical analysis indicates that a quantity-based commission plan can enhance the quantity-quality complementarity: the creators who reach a higher bracket of quantity should also produce a higher quality. This theoretical result is confirmed in multiple empirical tests. First, for a given book, the chapters published in the months when writers reached higher brackets of quantity had higher quality measured by chapter-to-chapter customer retention rates. Such a positive correlation is not significant in books published when the platform offered a uniform commission plan. Second, when writers produced higher quantity early on in a month but failed later, there was a greater quality drop under the quantity-based commission plan than under the uniform commission plan.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".