MEDIA LITERACY OF DIFFERENT POPULATION GROUPS IN USA AND CANADA IN INFORMATION SOCIETY
Bibliographic record
Abstract
In the article the author analyses programs of media education able to provide all population groups with possibility to acquire and develop digital and media competences, without which a citizen’s and specialist’s life is impossible nowadays. The article’s purpose is to analyze media education opportunities for average citizen in USA and Canada by means of informal and non formal education, characterize the ways, target groups and awaited results from acquiring media competences by American and Canadian citizens. Methodology. The research was carried out with the help of general scientific methods: analysis, synthesis, comparison and descriptive method. Scientific novelty is in determining target groups and awaited results from this media education (national minorities, students with special needs to enable them their own media projects as «voice» of community, young people, who were imprisoned for their active citizenship, newly come immigrants for their employment, elderly people for their self realization as media consumers). The author highlighted main directions of programs division according to certain groups of students (schools and university programs, library and Internet centers programs). Possible ways of acquiring media competences have been revealed (at home, in libraries, with help of special platforms). Conclusion has been made about advantages of informal and non formal education in digital and media competences development.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".