La « Media education » nella scuola : perché, come, che cosa insegnare dei média
Bibliographic record
Abstract
The 20th century, especially since it is defined as « the century of the media », presents new challenges to schools and education. During the '80s, especially in the English speaking world like Australia, Great Britain, Canada and USA, a particular thinking and educative proposal bas been developed with regard to the audiovisual media which has given rise to a world movement of media educators. World conventions which were organized since 1988 (Lausanne) to 2000 (Toronto) brought to light this movement. « Pedagogical theories and practice » which is an expression of this new educative commitment goes under the name Media Education. This article presents the motives, the content and the method of this subject. The author refers particularly to the Italian experience, which according to the research-action method has been expressed in three-year curricula of education to the media for the middle school. The study concludes with the proposal of a new professional personality, namely the media educator, which some Italian universities are already interested in (as for instance the Cattolica of Milan, the Sapienza of Rome and the Suor Orsola Benincasa University Institute of Naples) who have initiated a new three years degree course and a one year's post-degree specialization course.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".