Freedom and Responsibility: Discussion on Transmission Ethics From the Perspective of New Media
Bibliographic record
Abstract
New media represented by network and mobile phone is gradually changing the life of people. They have greatly satisfied with people’s freedom of information and speech, broken through official information restriction, promoted their own transmission advantages and manifested the power of public discourse of the masses. However, the phenomenon of ethical misconduct is common in the transmission process of new media and freedom and responsibility are malposed due to various reasons. In view of this, how to guarantee transmission freedom and undertake ethical responsibility has become a focus of study on media ethics currently. This paper starts from the current phenomenon of ethical misconduct in new media transmission to explore its reasons and puts forward measures for standardizing new media transmission ethics on this basis in terms of legal construction, media self-discipline and supervision & control.
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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 | 0.030 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.081 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| 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".