Issues of media education of the USA and Canada in the information society
Bibliographic record
Abstract
ABSTRACT The experience of different countries concerning the formation of preconditions for the development of the information society has been considered. The consequences of “totalitarian” model during the transition to open democratic civil society and the role of education and educators in this process have been defined on the example of Russia, Latvia, Lithuania and Estonia. The introduction of media education on the example of the U.S. and Canada as development of society and civilized economy has been substantiated. Didactic information space (DIS) - a unique educational technology, which was elaborated by scientists in the Mykolaiv College of Press and Television - has been characterized. It has proven to be the most efficient technology in the former Soviet Union that considers civic, social, educational direction, taking into account the information society. It has been determined that for further research it will be interesting to study interrelation between the need to obtain simultaneously a huge amount of experience - understanding European values, the next stage of human development (transition from industrial to information), the acquisition of democratic thinking, understanding of civil society - and the public acquisition of media literacy skills through the widespread introduction of media education. There exists the need of simultaneous acquisition of knowledge and skills that makes the process complicated. In addition, none of the considered aspects are taught in any school discipline and unfortunately they are submitted only in several universities of Ukraine. But there is a positive experimental experience of Mykolaiv College of Press and Television through the interrelation of classroom and extracurricular activities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".