Bibliographic record
Abstract
Marshall McLuhan, a famous Canadian communication scientist, has proposed a far-reaching view of media: media and information.Emphasizing that the media itself is the truly meaningful information, the basic driving force for social development, and the symbol to distinguish between different social forms.With the advent of the Internet era and the development of modern science and technology, people fragmented reading habits, short video as different from text, pictures, audio of a new media form, has become the audience, an important means, is gradually transformed into people's daily life, by the academic and industry, so myopic, frequency news as a new product under the background of the mobile Internet era, the research, has a strong practical significance and theoretical value.Most of the existing research on micro-documentaries focuses on the innovative exploration and narrative strategies in the era of financial media.This paper takes the fourth season of Momstant China as the analysis object, and adopts the sample analysis method and the literature research method to explore how micro-documentaries reflect on the form of the creation or the content of the audience, so as to help the creators achieve the best communication effect in the following works.It introduced the relevant concepts and meanings of the survival analysis method, and uses the hazard rate function, cumulative distribution function, survival function, and cumulative hazard rate function to describe the concept of survival time.Secondly, the application of survival analysis method in various industries at home and abroad is reviewed.Through the review of domestic and foreign applications, it is found that although the survival analysis method was first only applied in the medical field, it has been widely used by more and more scholars in other fields due to its advantages in processing the censored data.
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.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".