Road to micro-celebration: The role of mutation strategy of micro-celebrity in digital media
Bibliographic record
Abstract
The process of how ordinary people evolve to be well-known by delivering varied digital media content (i.e. micro-celebrification) remains perplexing. This study examines the role of mutation strategy featuring: (1) mutation diversity (the degree of evenness of content distribution across mutated styles) and (2) mutation divergence (i.e. the degree of inhomogeneity among mutated content styles), in predicting the success of micro-celebrification for ordinary people with varying talent levels. The results of survival analysis of a talent competition streamed on a major digital media platform in China suggest that a more diverse mutation in media content yields a higher chance of micro-celebrity success among participants in the competition. Interestingly, less talented participants benefit more from increasing mutation diversity compared with highly talented peers. Moreover, higher mutation divergence in the emotion evoked by media content increases the chance of success in micro-celebrification, opposite to that in the content genre and creator trait.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".