Situación de la educación en medios y la competencia crítica en el mundo actual: opinión de expertos internacionales
Bibliographic record
Abstract
The article analyzes the results of the international survey «Synthesis of Media Literacy Education and Media Criticism in the Modern World», conducted by the authors in May-July 2014. 64 media educators, media critics, and researchers in the field of media education and media culture participated in the survey, representing 18 countries: the USA, the UK, Canada, Australia, New Zealand, Germany, Ireland, Spain, Portugal, Sweden, Finland, Greece, Cyprus, Hungary, Ukraine, Serbia, Turkey, and Russia. Analysis of the data shows that the international expert community on the whole shares the view that the synthesis of media education and media criticism is not only possible, but also necessary, especially in terms of effectively developing the audience’s critical thinking skills. However, only 9.4% of the experts believe that media critics' texts are used in media literacy education classes in their countries to a large extent. Approximately one-third (34.4% of the polled experts) believe that this is happening at a moderate level, and about the same number (32.8%) believe that this is happening to a small extent. Consequently, media education and media criticism have a lot to work to do to make their synthesis really effective in the modern world.
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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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| 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".