Social Media as a Tool for the Development of Future Journalists’ Communicative Competence
Bibliographic record
Abstract
The aim of the work was to test the opportunities that social media offers on the Internet as a tool to improve the future journalists’ communicative competence. Sociological methods were involved in the research in order to achieve its aim: a method of testing respondents to identify their initial level of communicativeness on the Communicative Competence Test and the Questionnaire Survey Method to obtain feedback from participants in the experiment. Working on media content on the Internet proved to improve communication skills of future journalists with the help of popular YouTube media and blogs with bright visual content. It was found that media resources can be used during lectures to stimulate interest and to engage students in active learning that promote deeper knowledge. The possibility of involving online mass media in joint learning, problem solving, interactive lecture demonstrations, as well as discussions was noted. A number of difficulties that the course participants encountered were found: a thorough understanding of copyright law, increased workload, lack of skills in working with electronic text, video and audio content, increased visual load. However, these difficulties did not affect the quality of training and allowed the participants to improve not only communication skills but also digital information skills and online media literacy. It was proved that social media is a powerful additional tool that encourages students to actively study, get high results and sustainable professional skills that are in demand in the future workplace. The media cannot, however, replace traditional teaching methods which are based on personal communication. Further research is required on complex long-term courses that use a wider range of media, including Tik-Tok, Facebook, Instagram, extended blogs in the form of a multipage website, a funnel for gathering the target audience and subscribing.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".