The Joint Effects of Novelty and Familiarity on Creativity Adoption in Content Creation Platforms
Bibliographic record
Abstract
The rapid rise of content creation platforms in the digital age has provided new incubators for mass creativity, and also fundamentally reconfigured the production and consumption of creative works. For content creators seeking personal achievements on these platforms, how to produce works that are more likely to be adopted by platform users is a key concern. Based on optimal distinctiveness theory, this study draws on unobtrusive data of 174,053 posts from 2,450 content creators in a major Chinese content creation platform to explore the joint effects of novelty and familiarity of the creative works on creativity adoption. We constructed measurements of novelty and familiarity based on hashtags of the post, and conducted polynomial analysis and response surface analysis to test our hypotheses. Our findings complement existing creativity adoption research which mainly focus on the effects of novelty, and extend the explanatory power of optimal distinctiveness theory in the new context. We also offer practical guidance for both content creators and platform governors. Limitations and future directions are discussed.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".