Bibliographic record
Abstract
The purpose of the study is to consider memes as a component of media reality in scientific discourse and to find out the prospects of memo-creativity in the Ukrainian media space. Research methodology. The basis of the study is systematization, analysis and synthesis, interpretation methods. Results. Based on the analysis of the scientific literature, the autors characterize the meme as a visual image of media reality and communication unit are highlighted. The functions of memes and perspectives of memoir creation as a form of interaction with the audience are singled out and substantiated. Journalistic materials today are not limited of traditional forms, journalists use this type of media texts as a news meme. The form of memes allows you to briefly and accurately describe the phenomenon, fact, event, such visual images are involved in the formation of media reality. The authors analyzed such media projects as «Toronto Television» and «Novinach» is considered, each of which forms the agenda of the Ukrainian media space and creates a public resonance of facts. The components of meme success that contribute to its greatest popularity are highlighted. The existence of a meme is possible only with the participation of users who distribute it, comment on it, and its task is to provoke a reaction, to encourage the audience to interact. Practical significance. The results of the research can be used in the process of studying courses on communication, formation of media reality in the specialties «Journalism», «Advertising and Public Relations», «Information Business», as well as media practitioners in matters of interaction with the audience. Key words: meme, media reality, memoir, social network community, anti-discrimination practices, news, internet communication, media meme, digital media, visual image.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.013 |
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".