Analysis and Development Trend of Traditional Media and New Media Communication
Bibliographic record
Abstract
In the context of the new era, people's access to information has gradually been reformed with the development of science and technology. The wide application of network technology in new media communication not only improves the limitations of traditional media communication, but also effectively promotes the development of the news media communication industry to a higher level, efficiently realizes the instantaneous, large and comprehensive nature of information dissemination, and plays a huge role in the healthy and sustainable development of society. Since the 21st century, the rapid development of Internet information and digital technology, new media communication has also emerged. Cultural communication and news communication have ushered in new development opportunities, but also inevitably some challenges. Advances in digital technology are driving the digital transformation of traditional publications. The dissemination of culture and news is also increasingly the mainstream form of Internet culture and news new media communication. Many developed countries are actively using Internet technology to develop new media communication. This paper will analyse in detail the basic status quo of new media communication under the background of traditional media and the Internet era and look forward to its future development trend.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".