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Record W3216284197 · doi:10.54664/mulf5018

Experience of Canada, The United Kingdom, Finland and Spain for formation of media literacy among the students

2020· article· en· W3216284197 on OpenAlexaboutno aff
Zlatina Dimitrova

Bibliographic record

VenuePedagogical Almanac · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedia literacySubject (documents)KingdomLiteracyPolitical sciencePedagogySociologyMathematics educationMedia studiesPsychologyLibrary scienceComputer science

Abstract

fetched live from OpenAlex

The theoretical research focuses on the educational experience for the formation of media literacy among school-age children in different countries around the world. The article presents various options for the formation of media literacy, based on three educational models. According to the first model, media education is represented in the form of a compulsory subject in schools, which is studied by students in different grades. According to the second educational model, media habits are acquired within the interdisciplinary (integrated) approach – the use of the media in traditional school subjects, including native and foreign languages, literature, social sciences. The third model offers practical and informal integration of media education as a supplement and replacement of specific subjects or the intersection between them. The article examines in detail the media training opportunities offered in Canada, the United Kingdom, Finland and Spain, as their experience in media education is applied in a number of other countries around the world. Special attention is paid to the first steps in the introduction of media literacy training among students in Bulgaria, which is carried out only in the last 5-6 years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.002
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.150
GPT teacher head0.335
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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